Techniques for acquiring and processing statistics data in an image signal processor

ABSTRACT

Various techniques are disclosed for processing statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine may be implemented in a front-end processing unit of the ISP, such that statistics are collected prior to processing by an ISP pipeline downstream from the front-end processing unit. In one embodiment, the statistics collection engine may be configured to acquire statistics relating to auto white-balance, auto-exposure, and auto-focus, as well as flicker detection. Collected statistics may be output to a memory and used by the ISP to process acquired image data.

BACKGROUND

The present disclosure relates generally to digital imaging devices and,more particularly, to systems and method for processing image dataobtained using an image sensor of a digital imaging device.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present techniques,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

In recent years, digital imaging devices have become increasing populardue, at least in part, to such devices becoming more and more affordablefor the average consumer. Further, in addition to a number ofstand-alone digital cameras currently available on the market, it is notuncommon for digital imaging devices to be integrated as part of anotherelectronic device, such as a desktop or notebook computer, a cellularphone, or a portable media player.

To acquire image data, most digital imaging devices include an imagesensor that provides a number of light-detecting elements (e.g.,photodetectors) configured to convert light detected by the image sensorinto an electrical signal. An image sensor may also include a colorfilter array that filters light captured by the image sensor to capturecolor information. The image data captured by the image sensor may thenbe processed by an image processing pipeline, which may apply a numberof various image processing operations to the image data to generate afull color image that may be displayed for viewing on a display device,such as a monitor.

While conventional image processing techniques generally aim to producea viewable image that is both objectively and subjectively pleasing to aviewer, such conventional techniques may not adequately address errorsand/or distortions in the image data introduced by the imaging deviceand/or the image sensor. For instance, defective pixels on the imagesensor, which may be due to manufacturing defects or operationalfailure, may fail to sense light levels accurately and, if notcorrected, may manifest as artifacts appearing in the resultingprocessed image. Additionally, light intensity fall-off at the edges ofthe image sensor, which may be due to imperfections in the manufactureof the lens, may adversely affect characterization measurements and mayresult in an image in which the overall light intensity is non-uniform.The image processing pipeline may also perform one or more processes tosharpen the image. Conventional sharpening techniques, however, may notadequately account for existing noise in the image signal, or may beunable to distinguish the noise from edges and textured areas in theimage. In such instances, conventional sharpening techniques mayactually increase the appearance of noise in the image, which isgenerally undesirable. Further, various additional image processingsteps, some of which may rely on image statistics collected by astatistics collection engine, may also be performed.

Another image processing operation that may be applied to the image datacaptured by the image sensor is a demosaicing operation. Because thecolor filter array generally provides color data at one wavelength persensor pixel, a full set of color data is generally interpolated foreach color channel in order to reproduce a full color image (e.g., RGBimage). Conventional demosaicing techniques generally interpolate valuesfor the missing color data in a horizontal or a vertical direction,generally depending on some type of fixed threshold. However, suchconventional demosaicing techniques may not adequately account for thelocations and direction of edges within the image, which may result inedge artifacts, such as aliasing, checkerboard artifacts, or rainbowartifacts, being introduced into the full color image, particularlyalong diagonal edges within the image.

Accordingly, various considerations should be addressed when processinga digital image obtained with a digital camera or other imaging devicein order to improve the appearance of the resulting image. Inparticular, certain aspects of the disclosure below may address one ormore of the drawbacks briefly mentioned above.

SUMMARY

A summary of certain embodiments disclosed herein is set forth below. Itshould be understood that these aspects are presented merely to providethe reader with a brief summary of these certain embodiments and thatthese aspects are not intended to limit the scope of this disclosure.Indeed, this disclosure may encompass a variety of aspects that may notbe set forth below.

The present disclosure provides various techniques for collecting andprocessing statistics data in an image signal processor (ISP). In oneembodiment, a statistics collection engine may be implemented in afront-end processing unit of the ISP, such that statistics are collectedprior to processing by an ISP pipeline downstream from the front-endprocessing unit. In accordance with one aspect of the disclosure, thestatistics collection engine may be configured to acquire statisticsrelating to auto white-balance, auto-exposure, and auto-focus. In oneembodiment, the statistics collection engine may receive raw Bayer RGBdata acquired by an image sensor and may be configured to perform one ormore color space conversions to obtain pixel data in other color spaces.A set of pixel filters may be configured to accumulate sums of the pixeldata conditionally based upon YC1C2 characteristics, as defined by apixel condition per pixel filter. Depending on a selected color space,the pixel filters may generate color sums, which may be used to match acurrent illuminant against a set of reference illuminants with which theimage sensor has been previously calibrated.

In accordance with another aspect of the disclosure, auto-focusstatistics may be used to generate coarse and fine auto-focus scores fordetermining an optimal focal length at which to position a lensassociated with the image sensor. For instance, the statistics logic maydetermine a coarse position that indicates an optimal focus area which,in one embodiment, may be determined by searching for the first coarseposition in which a coarse auto-focus score decreases with respect to aprevious position. Using this position as a starting point for finescore searching, the optimal focal position may be determined bysearching for a peak in fine auto-focus scores. Auto-focus statisticsmay also be determined based on each color of the Bayer RGB, such that,even in the presence of chromatic aberrations, relative auto-focusscores for each color may be used to determine the direction of focus.Further, collected statistics may be output to a memory and used by theISP to process acquired image data.

Various refinements of the features noted above may exist in relation tovarious aspects of the present disclosure. Further features may also beincorporated in these various aspects as well. These refinements andadditional features may exist individually or in any combination. Forinstance, various features discussed below in relation to one or more ofthe illustrated embodiments may be incorporated into any of theabove-described aspects of the present disclosure alone or in anycombination. Again, the brief summary presented above is intended onlyto familiarize the reader with certain aspects and contexts ofembodiments of the present disclosure without limitation to the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee.

Various aspects of this disclosure may be better understood upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is a simplified block diagram depicting components of an exampleof an electronic device that includes an imaging device and imageprocessing circuitry configured to implement one or more of the imageprocessing technique set forth in the present disclosure;

FIG. 2 shows a graphical representation of a 2×2 pixel block of a Bayercolor filter array that may be implemented in the imaging device of FIG.1;

FIG. 3 is a perspective view of the electronic device of FIG. 1 in theform of a laptop computing device, in accordance with aspects of thepresent disclosure;

FIG. 4 is a front view of the electronic device of FIG. 1 in the form ofa desktop computing device, in accordance with aspects of the presentdisclosure;

FIG. 5 is a front view of the electronic device of FIG. 1 in the form ofa handheld portable electronic device, in accordance with aspects of thepresent disclosure;

FIG. 6 is a rear view of the electronic device shown in FIG. 5;

FIG. 7 is a block diagram illustrating front-end image signal processing(ISP) logic and ISP pipe processing logic that may be implemented in theimage processing circuitry of FIG. 1, in accordance with aspects of thepresent disclosure;

FIG. 8 is a more detailed block diagram showing an embodiment of the ISPfront-end logic of FIG. 7, in accordance with aspects of the presentdisclosure;

FIG. 9 is flow chart depicting a method for processing image data in theISP front-end logic of FIG. 8, in accordance with an embodiment

FIG. 10 is block diagram illustrating a configuration of double bufferedregisters and control registers that may be utilized for processingimage data in the ISP front-end logic, in accordance with oneembodiment;

FIGS. 11-13 are timing diagrams depicting different modes for triggeringthe processing of an image frame, in accordance with embodiments of thepresent techniques;

FIG. 14 is a diagram depicting a control register in more detail, inaccordance with one embodiment;

FIG. 15 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 8 is operating in a single sensor mode;

FIG. 16 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 8 is operating in a dual sensor mode;

FIG. 17 is a flow chart depicting a method for using a front-end pixelprocessing unit to process image frames when the ISP front-end logic ofFIG. 8 is operating in a dual sensor mode;

FIG. 18 is a flow chart depicting a method in which both image sensorsare active, but wherein a first image sensor is sending image frames toa front-end pixel processing unit, while the second image sensor issending image frames to a statistics processing unit so that imagingstatistics for the second sensor are immediately available when thesecond image sensor continues sending image frames to the front-endpixel processing unit at a later time, in accordance with oneembodiment.

FIG. 19 is graphical depiction of various imaging regions that may bedefined within a source image frame captured by an image sensor, inaccordance with aspects of the present disclosure;

FIG. 20 is a block diagram that provides a more detailed view of oneembodiment of the ISP front-end pixel processing unit, as shown in theISP front-end logic of FIG. 8, in accordance with aspects of the presentdisclosure;

FIG. 21 is a process diagram illustrating how temporal filtering may beapplied to image pixel data received by the ISP front-end pixelprocessing unit shown in FIG. 20, in accordance with one embodiment;

FIG. 22 illustrates a set of reference image pixels and a set ofcorresponding current image pixels that may be used to determine one ormore parameters for the temporal filtering process shown in FIG. 21;

FIG. 23 is a flow chart illustrating a process for applying temporalfiltering to a current image pixel of a set of image data, in accordancewith one embodiment;

FIG. 24 is a flow chart showing a technique for calculating a motiondelta value for use with the temporal filtering of the current imagepixel of FIG. 23, in accordance with one embodiment;

FIG. 25 is a flow chart illustrating another process for applyingtemporal filtering to a current image pixel of a set of image data thatincludes the use of different gains for each color component of theimage data, in accordance with another embodiment;

FIG. 26 is a process diagram illustrating a how a temporal filteringtechnique that utilizes separate motion and luma tables for each colorcomponent of the image pixel data received by the ISP front-end pixelprocessing unit shown in FIG. 20, in accordance with a furtherembodiment;

FIG. 27 is a flow chart illustrating a process for applying temporalfiltering to a current image pixel of a set of image data using themotion and luma tables shown in FIG. 26, in accordance with furtherembodiment;

FIG. 28 depicts a sample of full resolution raw image data that may becaptured by an image sensor, in accordance with aspects of the presentdisclosure;

FIG. 29 illustrates an image sensor that may be configured to applybinning to the full resolution raw image data of FIG. 28 to output asample of binned raw image data, in accordance with an embodiment of thepresent disclosure;

FIG. 30 depicts a sample of binned raw image data that may be providedby the image sensor of FIG. 29, in accordance with aspects of thepresent disclosure;

FIG. 31 depicts the binned raw image data from FIG. 30 after beingre-sampled by a binning compensation filter to provide, in accordancewith aspects of the present disclosure;

FIG. 32 depicts a binning compensation filter that may be implemented inthe ISP front-end pixel processing unit of FIG. 20, in accordance withone embodiment;

FIG. 33 is a graphical depiction of various step sizes that may beapplied to a differential analyzer to select center input pixels andindex/phases for binning compensation filtering, in accordance withaspects of the present disclosure;

FIG. 34 is a flow chart illustrating a process for scaling image datausing the binning compensation filter of FIG. 32, in accordance with oneembodiment;

FIG. 35 is a flow chart illustrating a process for determining a currentinput source center pixel for horizontal and vertical filtering by thebinning compensation filter of FIG. 32, in accordance with oneembodiment;

FIG. 36 is a flow chart illustrating a process for determining an indexfor selecting filtering coefficients for horizontal and verticalfiltering by the binning compensation filter of FIG. 32, in accordancewith one embodiment.

FIG. 37 is more a more detailed block diagram showing an embodiment of astatistics processing unit which may be implemented in the ISP front-endprocessing logic, as shown in FIG. 8, in accordance with aspects of thepresent disclosure;

FIG. 38 shows various image frame boundary cases that may be consideredwhen applying techniques for detecting and correcting defective pixelsduring statistics processing by the statistics processing unit of FIG.37, in accordance with aspects of the present disclosure;

FIG. 39 is a flow chart illustrating a process for performing defectivepixel detection and correction during statistics processing, inaccordance with one embodiment;

FIG. 40 shows a three-dimensional profile depicting light intensityversus pixel position for a conventional lens of an imaging device;

FIG. 41 is a colored drawing that exhibits non-uniform light intensityacross the image, which may be the result of lens shadingirregularities;

FIG. 42 is a graphical illustration of a raw imaging frame that includesa lens shading correction region and a gain grid, in accordance withaspects of the present disclosure;

FIG. 43 illustrates the interpolation of a gain value for an image pixelenclosed by four bordering grid gain points, in accordance with aspectsof the present disclosure;

FIG. 44 is a flow chart illustrating a process for determininginterpolated gain values that may be applied to imaging pixels during alens shading correction operation, in accordance with an embodiment ofthe present technique;

FIG. 45 is a three-dimensional profile depicting interpolated gainvalues that may be applied to an image that exhibits the light intensitycharacteristics shown in FIG. 40 when performing lens shadingcorrection, in accordance with aspects of the present disclosure;

FIG. 46 shows the colored drawing from FIG. 41 that exhibits improveduniformity in light intensity after a lens shading correction operationis applied, in accordance with accordance aspects of the presentdisclosure;

FIG. 47 graphically illustrates how a radial distance between a currentpixel and the center of an image may be calculated and used to determinea radial gain component for lens shading correction, in accordance withone embodiment;

FIG. 48 is a flow chart illustrating a process by which radial gains andinterpolated gains from a gain grid are used to determine a total gainthat may be applied to imaging pixels during a lens shading correctionoperation, in accordance with an embodiment of the present technique;

FIG. 49 is a graph showing white areas and low and high colortemperature axes in a color space;

FIG. 50 is a table showing how white balance gains may be configured forvarious reference illuminant conditions, in accordance with oneembodiment;

FIG. 51 is a block diagram showing a statistics collection engine thatmay be implemented in the ISP front-end processing logic, in accordancewith an embodiment of the present disclosure;

FIG. 52 illustrates the down-sampling of raw Bayer RGB data, inaccordance with aspects of the present disclosure;

FIG. 53 depicts a two-dimensional color histogram that may be collectedby the statistics collection engine of FIG. 51, in accordance with oneembodiment;

FIG. 54 depicts zooming and panning within a two-dimensional colorhistogram;

FIG. 55 is a more detailed view showing logic for implementing a pixelfilter of the statistics collection engine, in accordance with oneembodiment;

FIG. 56 is a graphical depiction of how the location of a pixel within aC1-C2 color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with one embodiment;

FIG. 57 is a graphical depiction of how the location of a pixel within aC1-C2 color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with another embodiment;

FIG. 58 is a graphical depiction of how the location of a pixel within aC1-C2 color space may be evaluated based on a pixel condition definedfor a pixel filter, in accordance with yet a further embodiment;

FIG. 59 is a graph showing how image sensor integration times may bedetermined to compensate for flicker, in accordance with one embodiment;

FIG. 60 is a detailed block diagram showing logic that may beimplemented in the statistics collection engine of FIG. 51 andconfigured to collect auto-focus statistics in accordance with oneembodiment;

FIG. 61 is a graph depicting a technique for performing auto-focus usingcoarse and fine auto-focus scoring values, in accordance with oneembodiment;

FIG. 62 is a flow chart depicting a process for performing auto-focususing coarse and fine auto-focus scoring values, in accordance with oneembodiment;

FIGS. 63 and 64 show the decimation of raw Bayer data to obtain a whitebalanced luma value;

FIG. 65 shows a technique for performing auto-focus using relativeauto-focus scoring values for each color component, in accordance withone embodiment;

FIG. 66 is a more detailed view of the statistics processing unit ofFIG. 37, showing how Bayer RGB histogram data may be used to assistblack level compensation, in accordance with one embodiment;

FIG. 67 is a block diagram showing an embodiment of the ISP pipeprocessing logic of FIG. 7, in accordance with aspects of the presentdisclosure;

FIG. 68 is a more detailed view showing an embodiment of a raw pixelprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 67, in accordance with aspects of the present disclosure;

FIG. 69 shows various image frame boundary cases that may be consideredwhen applying techniques for detecting and correcting defective pixelsduring processing by the raw pixel processing block shown in FIG. 68, inaccordance with aspects of the present disclosure;

FIGS. 70-72 are flowcharts that depict various processes for detectingand correcting defective pixels that may be performed in the raw pixelprocessing block of FIG. 68, in accordance with one embodiment;

FIG. 73 shows the location of two green pixels in a 2×2 pixel block of aBayer image sensor that may be interpolated when applying greennon-uniformity correction techniques during processing by the raw pixelprocessing logic of FIG. 68, in accordance with aspects of the presentdisclosure;

FIG. 74 illustrates a set of pixels that includes a center pixel andassociated horizontal neighboring pixels that may be used as part of ahorizontal filtering process for noise reduction, in accordance withaspects of the present disclosure;

FIG. 75 illustrates a set of pixels that includes a center pixel andassociated vertical neighboring pixels that may be used as part of avertical filtering process for noise reduction, in accordance withaspects of the present disclosure;

FIG. 76 is a simplified flow diagram that depicts how demosaicing may beapplied to a raw Bayer image pattern to produce a full color RGB image;

FIG. 77 depicts a set of pixels of a Bayer image pattern from whichhorizontal and vertical energy components may be derived forinterpolating green color values during demosaicing of the Bayer imagepattern, in accordance with one embodiment;

FIG. 78 shows a set of horizontal pixels to which filtering may beapplied to determine a horizontal component of an interpolated greencolor value during demosaicing of a Bayer image pattern, in accordancewith aspects of the present technique;

FIG. 79 shows a set of vertical pixels to which filtering may be appliedto determine a vertical component of an interpolated green color valueduring demosaicing of a Bayer image pattern, in accordance with aspectsof the present technique;

FIG. 80 shows various 3×3 pixel blocks to which filtering may be appliedto determine interpolated red and blue values during demosaicing of aBayer image pattern, in accordance with aspects of the presenttechnique;

FIGS. 81-84 provide flowcharts that depict various processes forinterpolating green, red, and blue color values during demosaicing of aBayer image pattern, in accordance with one embodiment;

FIG. 85 shows a colored drawing of an original image scene that may becaptured by an image sensor and processed in accordance with aspects ofthe demosaicing techniques disclosed herein;

FIG. 86 shows a colored drawing of Bayer image pattern of the imagescene shown in FIG. 85;

FIG. 87 shows a colored drawing of an RGB image reconstructed using aconventional demosaicing technique based upon the Bayer image pattern ofFIG. 86;

FIG. 88 shows a colored drawing of an RGB image reconstructed from theBayer image pattern of FIG. 86 in accordance with aspects of thedemosaicing techniques disclosed herein;

FIG. 89 is a more detailed view showing one embodiment of an RGBprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 67, in accordance with aspects of the present disclosure;

FIG. 90 is a more detailed view showing one embodiment of a YCbCrprocessing block that may be implemented in the ISP pipe processinglogic of FIG. 67, in accordance with aspects of the present disclosure;

FIG. 91 is a graphical depiction of active source regions for luma andchroma, as defined within a source buffer using a 1-plane format, inaccordance with aspects of the present disclosure;

FIG. 92 is a graphical depiction of active source regions for luma andchroma, as defined within a source buffer using a 2-plane format, inaccordance with aspects of the present disclosure;

FIG. 93 is a block diagram illustrating image sharpening logic that maybe implemented in the YCbCr processing block, as shown in FIG. 90, inaccordance with one embodiment;

FIG. 94 is a block diagram illustrating edge enhancement logic that maybe implemented in the YCbCr processing block, as shown in FIG. 90, inaccordance with one embodiment;

FIG. 95 is a graph showing the relationship of chroma attenuationfactors to sharpened luma values, in accordance with aspects of thepresent disclosure;

FIG. 96 is a block diagram illustrating image brightness, contrast, andcolor (BCC) adjustment logic that may be implemented in the YCbCrprocessing block, as shown in FIG. 90, in accordance with oneembodiment; and

FIG. 97 shows a hue and saturation color wheel in the YCbCr color spacedefining various hue angles and saturation values that may be appliedduring color adjustment in the BCC adjustment logic shown in FIG. 96.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

One or more specific embodiments of the present disclosure will bedescribed below. These described embodiments are only examples of thepresently disclosed techniques. Additionally, in an effort to provide aconcise description of these embodiments, all features of an actualimplementation may not be described in the specification. It should beappreciated that in the development of any such actual implementation,as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Additionally, it should be understood that references to “oneembodiment” or “an embodiment” of the present disclosure are notintended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

As will be discussed below, the present disclosure relates generally totechniques for processing image data acquired via one or more imagesensing devices. In particular, certain aspects of the presentdisclosure may relate to techniques for detecting and correctingdefective pixels, techniques for demosaicing a raw image pattern,techniques for sharpening a luminance image using a multi-scale unsharpmask, and techniques for applying lens shading gains to correct for lensshading irregularities. Further, it should be understood that thepresently disclosed techniques may be applied to both still images andmoving images (e.g., video), and may be utilized in any suitable type ofimaging application, such as a digital camera, an electronic devicehaving an integrated digital camera, a security or video surveillancesystem, a medical imaging system, and so forth.

Keeping the above points in mind, FIG. 1 is a block diagram illustratingan example of an electronic device 10 that may provide for theprocessing of image data using one or more of the image processingtechniques briefly mentioned above. The electronic device 10 may be anytype of electronic device, such as a laptop or desktop computer, amobile phone, a digital media player, or the like, that is configured toreceive and process image data, such as data acquired using one or moreimage sensing components. By way of example only, the electronic device10 may be a portable electronic device, such as a model of an iPod® oriPhone®, available from Apple Inc. of Cupertino, Calif. Additionally,the electronic device 10 may be a desktop or laptop computer, such as amodel of a MacBook®, MacBook® Pro, MacBook Air®, iMac®, Mac® Mini, orMac Pro®, available from Apple Inc. In other embodiments, electronicdevice 10 may also be a model of an electronic device from anothermanufacturer that is capable of acquiring and processing image data.

Regardless of its form (e.g., portable or non-portable), it should beunderstood that the electronic device 10 may provide for the processingof image data using one or more of the image processing techniquesbriefly discussed above, which may include defective pixel correctionand/or detection techniques, lens shading correction techniques,demosaicing techniques, or image sharpening techniques, among others. Insome embodiments, the electronic device 10 may apply such imageprocessing techniques to image data stored in a memory of the electronicdevice 10. In further embodiments, the electronic device 10 may includeone or more imaging devices, such as an integrated or external digitalcamera, configured to acquire image data, which may then be processed bythe electronic device 10 using one or more of the above-mentioned imageprocessing techniques. Embodiments showing both portable andnon-portable embodiments of electronic device 10 will be furtherdiscussed below in FIGS. 3-6.

As shown in FIG. 1, the electronic device 10 may include variousinternal and/or external components which contribute to the function ofthe device 10. Those of ordinary skill in the art will appreciate thatthe various functional blocks shown in FIG. 1 may comprise hardwareelements (including circuitry), software elements (including computercode stored on a computer-readable medium) or a combination of bothhardware and software elements. For example, in the presentlyillustrated embodiment, the electronic device 10 may includeinput/output (I/O) ports 12, input structures 14, one or more processors16, memory device 18, non-volatile storage 20, expansion card(s) 22,networking device 24, power source 26, and display 28. Additionally, theelectronic device 10 may include one or more imaging devices 30, such asa digital camera, and image processing circuitry 32. As will bediscussed further below, the image processing circuitry 32 may beconfigured implement one or more of the above-discussed image processingtechniques when processing image data. As can be appreciated, image dataprocessed by image processing circuitry 32 may be retrieved from thememory 18 and/or the non-volatile storage device(s) 20, or may beacquired using the imaging device 30.

Before continuing, it should be understood that the system block diagramof the device 10 shown in FIG. 1 is intended to be a high-level controldiagram depicting various components that may be included in such adevice 10. That is, the connection lines between each individualcomponent shown in FIG. 1 may not necessarily represent paths ordirections through which data flows or is transmitted between variouscomponents of the device 10. Indeed, as discussed below, the depictedprocessor(s) 16 may, in some embodiments, include multiple processors,such as a main processor (e.g., CPU), and dedicated image and/or videoprocessors. In such embodiments, the processing of image data may beprimarily handled by these dedicated processors, thus effectivelyoffloading such tasks from a main processor (CPU).

With regard to each of the illustrated components in FIG. 1, the I/Oports 12 may include ports configured to connect to a variety ofexternal devices, such as a power source, an audio output device (e.g.,headset or headphones), or other electronic devices (such as handhelddevices and/or computers, printers, projectors, external displays,modems, docking stations, and so forth). In one embodiment, the I/Oports 12 may be configured to connect to an external imaging device,such as a digital camera, for the acquisition of image data that may beprocessed using the image processing circuitry 32. The I/O ports 12 maysupport any suitable interface type, such as a universal serial bus(USB) port, a serial connection port, an IEEE-1394 (FireWire) port, anEthernet or modem port, and/or an AC/DC power connection port.

In some embodiments, certain I/O ports 12 may be configured to providefor more than one function. For instance, in one embodiment, the I/Oports 12 may include a proprietary port from Apple Inc. that mayfunction not only to facilitate the transfer of data between theelectronic device 10 and an external source, but also to couple thedevice 10 to a power charging interface such as an power adapterdesigned to provide power from a electrical wall outlet, or an interfacecable configured to draw power from another electrical device, such as adesktop or laptop computer, for charging the power source 26 (which mayinclude one or more rechargeable batteries). Thus, the I/O port 12 maybe configured to function dually as both a data transfer port and anAC/DC power connection port depending, for example, on the externalcomponent being coupled to the device 10 via the I/O port 12.

The input structures 14 may provide user input or feedback to theprocessor(s) 16. For instance, input structures 14 may be configured tocontrol one or more functions of electronic device 10, such asapplications running on electronic device 10. By way of example only,input structures 14 may include buttons, sliders, switches, controlpads, keys, knobs, scroll wheels, keyboards, mice, touchpads, and soforth, or some combination thereof. In one embodiment, input structures14 may allow a user to navigate a graphical user interface (GUI)displayed on device 10. Additionally, input structures 14 may include atouch sensitive mechanism provided in conjunction with display 28. Insuch embodiments, a user may select or interact with displayed interfaceelements via the touch sensitive mechanism.

The input structures 14 may include the various devices, circuitry, andpathways by which user input or feedback is provided to one or moreprocessors 16. Such input structures 14 may be configured to control afunction of the device 10, applications running on the device 10, and/orany interfaces or devices connected to or used by the electronic device10. For example, the input structures 14 may allow a user to navigate adisplayed user interface or application interface. Examples of the inputstructures 14 may include buttons, sliders, switches, control pads,keys, knobs, scroll wheels, keyboards, mice, touchpads, and so forth.

In certain embodiments, an input structure 14 and the display device 28may be provided together, such as in the case of a “touchscreen,”whereby a touch-sensitive mechanism is provided in conjunction with thedisplay 28. In such embodiments, the user may select or interact withdisplayed interface elements via the touch-sensitive mechanism. In thisway, the displayed interface may provide interactive functionality,allowing a user to navigate the displayed interface by touching thedisplay 28. For example, user interaction with the input structures 14,such as to interact with a user or application interface displayed onthe display 26, may generate electrical signals indicative of the userinput. These input signals may be routed via suitable pathways, such asan input hub or data bus, to the one or more processors 16 for furtherprocessing.

In addition to processing various input signals received via the inputstructure(s) 14, the processor(s) 16 may control the general operationof the device 10. For instance, the processor(s) 16 may provide theprocessing capability to execute an operating system, programs, user andapplication interfaces, and any other functions of the electronic device10. The processor(s) 16 may include one or more microprocessors, such asone or more “general-purpose” microprocessors, one or morespecial-purpose microprocessors and/or application-specificmicroprocessors (ASICs), or a combination of such processing components.For example, the processor(s) 16 may include one or more instruction set(e.g., RISC) processors, as well as graphics processors (GPU), videoprocessors, audio processors and/or related chip sets. As will beappreciated, the processor(s) 16 may be coupled to one or more databuses for transferring data and instructions between various componentsof the device 10. In certain embodiments, the processor(s) 16 mayprovide the processing capability to execute an imaging applications onthe electronic device 10, such as Photo Booth®, Aperture®, iPhoto®, orPreview®, available from Apple Inc., or the “Camera” and/or “Photo”applications provided by Apple Inc. and available on models of theiPhone®.

The instructions or data to be processed by the processor(s) 16 may bestored in a computer-readable medium, such as a memory device 18. Thememory device 18 may be provided as a volatile memory, such as randomaccess memory (RAM) or as a non-volatile memory, such as read-onlymemory (ROM), or as a combination of one or more RAM and ROM devices.The memory 18 may store a variety of information and may be used forvarious purposes. For example, the memory 18 may store firmware for theelectronic device 10, such as a basic input/output system (BIOS), anoperating system, various programs, applications, or any other routinesthat may be executed on the electronic device 10, including userinterface functions, processor functions, and so forth. In addition, thememory 18 may be used for buffering or caching during operation of theelectronic device 10. For instance, in one embodiment, the memory 18include one or more frame buffers for buffering video data as it isbeing output to the display 28.

In addition to the memory device 18, the electronic device 10 mayfurther include a non-volatile storage 20 for persistent storage of dataand/or instructions. The non-volatile storage 20 may include flashmemory, a hard drive, or any other optical, magnetic, and/or solid-statestorage media, or some combination thereof. Thus, although depicted as asingle device in FIG. 1 for purposes of clarity, it should understoodthat the non-volatile storage device(s) 20 may include a combination ofone or more of the above-listed storage devices operating in conjunctionwith the processor(s) 16. The non-volatile storage 20 may be used tostore firmware, data files, image data, software programs andapplications, wireless connection information, personal information,user preferences, and any other suitable data. In accordance withaspects of the present disclosure, image data stored in the non-volatilestorage 20 and/or the memory device 18 may be processed by the imageprocessing circuitry 32 prior to being output on a display.

The embodiment illustrated in FIG. 1 may also include one or more cardor expansion slots. The card slots may be configured to receive anexpansion card 22 that may be used to add functionality, such asadditional memory, I/O functionality, or networking capability, to theelectronic device 10. Such an expansion card 22 may connect to thedevice through any type of suitable connector, and may be accessedinternally or external with respect to a housing of the electronicdevice 10. For example, in one embodiment, the expansion card 24 may beflash memory card, such as a SecureDigital (SD) card, mini- or microSD,CompactFlash card, or the like, or may be a PCMCIA device. Additionally,the expansion card 24 may be a Subscriber Identity Module (SIM) card,for use with an embodiment of the electronic device 10 that providesmobile phone capability.

The electronic device 10 also includes the network device 24, which maybe a network controller or a network interface card (NIC) that mayprovide for network connectivity over a wireless 802.11 standard or anyother suitable networking standard, such as a local area network (LAN),a wide area network (WAN), such as an Enhanced Data Rates for GSMEvolution (EDGE) network, a 3G data network, or the Internet. In certainembodiments, the network device 24 may provide for a connection to anonline digital media content provider, such as the iTunes® musicservice, available from Apple Inc.

The power source 26 of the device 10 may include the capability to powerthe device 10 in both non-portable and portable settings. For example,in a portable setting, the device 10 may include one or more batteries,such as a Li-Ion battery, for powering the device 10. The battery may bere-charged by connecting the device 10 to an external power source, suchas to an electrical wall outlet. In a non-portable setting, the powersource 26 may include a power supply unit (PSU) configured to draw powerfrom an electrical wall outlet, and to distribute the power to variouscomponents of a non-portable electronic device, such as a desktopcomputing system.

The display 28 may be used to display various images generated by device10, such as a GUI for an operating system, or image data (includingstill images and video data) processed by the image processing circuitry32, as will be discussed further below. As mentioned above, the imagedata may include image data acquired using the imaging device 30 orimage data retrieved from the memory 18 and/or non-volatile storage 20.The display 28 may be any suitable type of display, such as a liquidcrystal display (LCD), plasma display, or an organic light emittingdiode (OLED) display, for example. Additionally, as discussed above, thedisplay 28 may be provided in conjunction with the above-discussedtouch-sensitive mechanism (e.g., a touch screen) that may function aspart of a control interface for the electronic device 10.

The illustrated imaging device(s) 30 may be provided as a digital cameraconfigured to acquire both still images and moving images (e.g., video).The camera 30 may include a lens and one or more image sensorsconfigured to capturing and converting light into electrical signals. Byway of example only, the image sensor may include a CMOS image sensor(e.g., a CMOS active-pixel sensor (APS)) or a CCD (charge-coupleddevice) sensor. Generally, the image sensor in the camera 30 includes anintegrated circuit having an array of pixels, wherein each pixelincludes a photodetector for sensing light. As those skilled in the artwill appreciate, the photodetectors in the imaging pixels generallydetect the intensity of light captured via the camera lenses. However,photodetectors, by themselves, are generally unable to detect thewavelength of the captured light and, thus, are unable to determinecolor information.

Accordingly, the image sensor may further include a color filter array(CFA) that may overlay or be disposed over the pixel array of the imagesensor to capture color information. The color filter array may includean array of small color filters, each of which may overlap a respectivepixel of the image sensor and filter the captured light by wavelength.Thus, when used in conjunction, the color filter array and thephotodetectors may provide both wavelength and intensity informationwith regard to light captured through the camera, which may berepresentative of a captured image.

In one embodiment, the color filter array may include a Bayer colorfilter array, which provides a filter pattern that is 50% greenelements, 25% red elements, and 25% blue elements. For instance, FIG. 2shows a 2×2 pixel block of a Bayer CFA includes 2 green elements (Gr andGb), 1 red element (R), and 1 blue element (B). Thus, an image sensorthat utilizes a Bayer color filter array may provide informationregarding the intensity of the light received by the camera 30 at thegreen, red, and blue wavelengths, whereby each image pixel records onlyone of the three colors (RGB). This information, which may be referredto as “raw image data” or data in the “raw domain,” may then beprocessed using one or more demosaicing techniques to convert the rawimage data into a full color image, generally by interpolating a set ofred, green, and blue values for each pixel. As will be discussed furtherbelow, such demosaicing techniques may be performed by the imageprocessing circuitry 32.

As mentioned above, the image processing circuitry 32 may provide forvarious image processing steps, such as defective pixeldetection/correction, lens shading correction, demosaicing, and imagesharpening, noise reduction, gamma correction, image enhancement,color-space conversion, image compression, chroma sub-sampling, andimage scaling operations, and so forth. In some embodiments, the imageprocessing circuitry 32 may include various subcomponents and/ordiscrete units of logic that collectively form an image processing“pipeline” for performing each of the various image processing steps.These subcomponents may be implemented using hardware (e.g., digitalsignal processors or ASICs) or software, or via a combination ofhardware and software components. The various image processingoperations that may be provided by the image processing circuitry 32and, particularly those processing operations relating to defectivepixel detection/correction, lens shading correction, demosaicing, andimage sharpening, will be discussed in greater detail below.

Before continuing, it should be noted that while various embodiments ofthe various image processing techniques discussed below may utilize aBayer CFA, the presently disclosed techniques are not intended to belimited in this regard. Indeed, those skilled in the art will appreciatethat the image processing techniques provided herein may be applicableto any suitable type of color filter array, including RGBW filters, CYGMfilters, and so forth.

Referring again to the electronic device 10, FIGS. 3-6 illustratevarious forms that the electronic device 10 may take. As mentionedabove, the electronic device 10 may take the form of a computer,including computers that are generally portable (such as laptop,notebook, and tablet computers) as well as computers that are generallynon-portable (such as desktop computers, workstations and/or servers),or other type of electronic device, such as handheld portable electronicdevices (e.g., digital media player or mobile phone). In particular,FIGS. 3 and 4 depict the electronic device 10 in the form of a laptopcomputer 40 and a desktop computer 50, respectively. FIGS. 5 and 6 showfront and rear views, respectively, of the electronic device 10 in theform of a handheld portable device 60.

As shown in FIG. 3, the depicted laptop computer 40 includes a housing42, the display 28, the I/O ports 12, and the input structures 14. Theinput structures 14 may include a keyboard and a touchpad mouse that areintegrated with the housing 42. Additionally, the input structure 14 mayinclude various other buttons and/or switches which may be used tointeract with the computer 40, such as to power on or start thecomputer, to operate a GUI or an application running on the computer 40,as well as adjust various other aspects relating to operation of thecomputer 40 (e.g., sound volume, display brightness, etc.). The computer40 may also include various I/O ports 12 that provide for connectivityto additional devices, as discussed above, such as a FireWire® or USBport, a high definition multimedia interface (HDMI) port, or any othertype of port that is suitable for connecting to an external device.Additionally, the computer 40 may include network connectivity (e.g.,network device 26), memory (e.g., memory 20), and storage capabilities(e.g., storage device 22), as described above with respect to FIG. 1.

Further, the laptop computer 40, in the illustrated embodiment, mayinclude an integrated imaging device 30 (e.g., camera). In otherembodiments, the laptop computer 40 may utilize an external camera(e.g., an external USB camera or a “webcam”) connected to one or more ofthe I/O ports 12 instead of or in addition to the integrated camera 30.For instance, an external camera may be an iSight® camera available fromApple Inc. The camera 30, whether integrated or external, may providefor the capture and recording of images. Such images may then be viewedby a user using an image viewing application, or may be utilized byother applications, including video-conferencing applications, such asiChat®, and image editing/viewing applications, such as Photo Booth®,Aperture®, iPhoto®, or Preview®, which are available from Apple Inc. Incertain embodiments, the depicted laptop computer 40 may be a model of aMacBook®, MacBook® Pro, MacBook Air®, or PowerBook® available from AppleInc. Additionally, the computer 40, in one embodiment, may be a portabletablet computing device, such as a model of an iPad® tablet computer,also available from Apple Inc.

FIG. 4 further illustrates an embodiment in which the electronic device10 is provided as a desktop computer 50. As will be appreciated, thedesktop computer 50 may include a number of features that may begenerally similar to those provided by the laptop computer 40 shown inFIG. 4, but may have a generally larger overall form factor. As shown,the desktop computer 50 may be housed in an enclosure 42 that includesthe display 28, as well as various other components discussed above withregard to the block diagram shown in FIG. 1. Further, the desktopcomputer 50 may include an external keyboard and mouse (input structures14) that may be coupled to the computer 50 via one or more I/O ports 12(e.g., USB) or may communicate with the computer 50 wirelessly (e.g.,RF, Bluetooth, etc.). The desktop computer 50 also includes an imagingdevice 30, which may be an integrated or external camera, as discussedabove. In certain embodiments, the depicted desktop computer 50 may be amodel of an iMac®, Mac® mini, or Mac Pro®, available from Apple Inc.

As further shown, the display 28 may be configured to generate variousimages that may be viewed by a user. For example, during operation ofthe computer 50, the display 28 may display a graphical user interface(“GUI”) 52 that allows the user to interact with an operating systemand/or application running on the computer 50. The GUI 52 may includevarious layers, windows, screens, templates, or other graphical elementsthat may be displayed in all, or a portion, of the display device 28.For instance, in the depicted embodiment, an operating system GUI 52 mayinclude various graphical icons 54, each of which may correspond tovarious applications that may be opened or executed upon detecting auser selection (e.g., via keyboard/mouse or touchscreen input). Theicons 54 may be displayed in a dock 56 or within one or more graphicalwindow elements 58 displayed on the screen. In some embodiments, theselection of an icon 54 may lead to a hierarchical navigation process,such that selection of an icon 54 leads to a screen or opens anothergraphical window that includes one or more additional icons or other GUIelements. By way of example only, the operating system GUI 52 displayedin FIG. 4 may be from a version of the Mac OS® operating system,available from Apple Inc.

Continuing to FIGS. 5 and 6, the electronic device 10 is furtherillustrated in the form of portable handheld electronic device 60, whichmay be a model of an iPod® or iPhone® available from Apple Inc. In thedepicted embodiment, the handheld device 60 includes an enclosure 42,which may function to protect the interior components from physicaldamage and to shield them from electromagnetic interference. Theenclosure 42 may be formed from any suitable material or combination ofmaterials, such as plastic, metal, or a composite material, and mayallow certain frequencies of electromagnetic radiation, such as wirelessnetworking signals, to pass through to wireless communication circuitry(e.g., network device 24), which may be disposed within the enclosure42, as shown in FIG. 5.

The enclosure 42 also includes various user input structures 14 throughwhich a user may interface with the handheld device 60. For instance,each input structure 14 may be configured to control one or morerespective device functions when pressed or actuated. By way of example,one or more of the input structures 14 may be configured to invoke a“home” screen 42 or menu to be displayed, to toggle between a sleep,wake, or powered on/off mode, to silence a ringer for a cellular phoneapplication, to increase or decrease a volume output, and so forth. Itshould be understood that the illustrated input structures 14 are merelyexemplary, and that the handheld device 60 may include any number ofsuitable user input structures existing in various forms includingbuttons, switches, keys, knobs, scroll wheels, and so forth.

As shown in FIG. 5, the handheld device 60 may include various I/O ports12. For instance, the depicted I/O ports 12 may include a proprietaryconnection port 12 a for transmitting and receiving data files or forcharging a power source 26 and an audio connection port 12 b forconnecting the device 60 to an audio output device (e.g., headphones orspeakers). Further, in embodiments where the handheld device 60 providesmobile phone functionality, the device 60 may include an I/O port 12 cfor receiving a subscriber identify module (SIM) card (e.g., anexpansion card 22).

The display device 28, which may be an LCD, OLED, or any suitable typeof display, may display various images generated by the handheld device60. For example, the display 28 may display various system indicators 64providing feedback to a user with regard to one or more states ofhandheld device 60, such as power status, signal strength, externaldevice connections, and so forth. The display may also display a GUI 52that allows a user to interact with the device 60, as discussed abovewith reference to FIG. 4. The GUI 52 may include graphical elements,such as the icons 54 which may correspond to various applications thatmay be opened or executed upon detecting a user selection of arespective icon 54. By way of example, one of the icons 54 may representa camera application 66 that may be used in conjunction with a camera 30(shown in phantom lines in FIG. 5) for acquiring images. Referringbriefly to FIG. 6, a rear view of the handheld electronic device 60depicted in FIG. 5 is illustrated, which shows the camera 30 as beingintegrated with the housing 42 and positioned on the rear of thehandheld device 60.

As mentioned above, image data acquired using the camera 30 may beprocessed using the image processing circuitry 32, which my includehardware (e.g., disposed within the enclosure 42) and/or software storedon one or more storage devices (e.g., memory 18 or non-volatile storage20) of the device 60. Images acquired using the camera application 66and the camera 30 may be stored on the device 60 (e.g., in storagedevice 20) and may be viewed at a later time using a photo viewingapplication 68.

The handheld device 60 may also include various audio input and outputelements. For example, the audio input/output elements, depictedgenerally by reference numeral 70, may include an input receiver, suchas one or more microphones. For instance, where the handheld device 60includes cell phone functionality, the input receivers may be configuredto receive user audio input, such as a user's voice. Additionally, theaudio input/output elements 70 may include one or more outputtransmitters. Such output transmitters may include one or more speakerswhich may function to transmit audio signals to a user, such as duringthe playback of music data using a media player application 72. Further,in embodiments where the handheld device 60 includes a cell phoneapplication, an additional audio output transmitter 74 may be provided,as shown in FIG. 5. Like the output transmitters of the audioinput/output elements 70, the output transmitter 74 may also include oneor more speakers configured to transmit audio signals to a user, such asvoice data received during a telephone call. Thus, the audioinput/output elements 70 and 74 may operate in conjunction to functionas the audio receiving and transmitting elements of a telephone.

Having now provided some context with regard to various forms that theelectronic device 10 may take, the present discussion will now focus onthe image processing circuitry 32 depicted in FIG. 1. As mentionedabove, the image processing circuitry 32 may be implemented usinghardware and/or software components, and may include various processingunits that define an image signal processing (ISP) pipeline. Inparticular, the following discussion may focus on aspects of the imageprocessing techniques set forth in the present disclosure, particularlythose relating to defective pixel detection/correction techniques, lensshading correction techniques, demosaicing techniques, and imagesharpening techniques.

Referring now to FIG. 7, a simplified top-level block diagram depictingseveral functional components that may be implemented as part of theimage processing circuitry 32 is illustrated, in accordance with oneembodiment of the presently disclosed techniques. Particularly, FIG. 7is intended to illustrate how image data may flow through the imageprocessing circuitry 32, in accordance with at least one embodiment. Inorder to provide a general overview of the image processing circuitry32, a general description of how these functional components operate toprocess image data is provided here with reference to FIG. 7, while amore specific description of each of the illustrated functionalcomponents, as well as their respective sub-components, will be furtherprovided below.

Referring to the illustrated embodiment, the image processing circuitry32 may include image signal processing (ISP) front-end processing logic80, ISP pipe processing logic 82, and control logic 84. Image datacaptured by the imaging device 30 may first be processed by the ISPfront-end logic 80 and analyzed to capture image statistics that may beused to determine one or more control parameters for the ISP pipe logic82 and/or the imaging device 30. The ISP front-end logic 80 may beconfigured to capture image data from an image sensor input signal. Forinstance, as shown in FIG. 7, the imaging device 30 may include a camerahaving one or more lenses 88 and image sensor(s) 90. As discussed above,the image sensor(s) 90 may include a color filter array (e.g., a Bayerfilter) and may thus provide both light intensity and wavelengthinformation captured by each imaging pixel of the image sensors 90 toprovide for a set of raw image data that may be processed by the ISPfront-end logic 80. For instance, the output 92 from the imaging device30 may be received by a sensor interface 94, which may then provide theraw image data 96 to the ISP front-end logic 80 based, for example, onthe sensor interface type. By way of example, the sensor interface 94may utilize a Standard Mobile Imaging Architecture (SMIA) interface orother serial or parallel camera interfaces, or some combination thereof.In certain embodiments, the ISP front-end logic 80 may operate withinits own clock domain and may provide an asynchronous interface to thesensor interface 94 to support image sensors of different sizes andtiming requirements.

The raw image data 96 may be provided to the ISP front-end logic 80 andprocessed on a pixel-by-pixel basis in a number of formats. Forinstance, each image pixel may have a bit-depth of 8, 10, 12, or 14bits. The ISP front-end logic 80 may perform one or more imageprocessing operations on the raw image data 96, as well as collectstatistics about the image data 96. The image processing operations, aswell as the collection of statistical data, may be performed at the sameor at different bit-depth precisions. For example, in one embodiment,processing of the raw image pixel data 96 may be performed at aprecision of 14-bits. In such embodiments, raw pixel data received bythe ISP front-end logic 80 that has a bit-depth of less than 14 bits(e.g., 8-bit, 10-bit, 12-bit) may be up-sampled to 14-bits for imageprocessing purposes. In another embodiment, statistical processing mayoccur at a precision of 8-bits and, thus, raw pixel data having a higherbit-depth may be down-sampled to an 8-bit format for statisticspurposes. As will be appreciated, down-sampling to 8-bits may reducehardware size (e.g., area) and also reduce processing/computationalcomplexity for the statistics data. Additionally, the raw image data maybe averaged spatially to allow for the statistics data to be more robustto noise.

Further, as shown in FIG. 7, the ISP front-end logic 80 may also receivepixel data from the memory 108. For instance, as shown by referencenumber 98, the raw pixel data may be sent to the memory 108 from thesensor interface 94. The raw pixel data residing in the memory 108 maythen be provided to the ISP front-end logic 80 for processing, asindicated by reference number 100. The memory 108 may be part of thememory device 18, the storage device 20, or may be a separate dedicatedmemory within the electronic device 10 and may include direct memoryaccess (DMA) features. Further, in certain embodiments, the ISPfront-end logic 80 may operate within its own clock domain and providean asynchronous interface to the sensor interface 94 to support sensorsof different sizes and having different timing requirements.

Upon receiving the raw image data 96 (from sensor interface 94) or 100(from memory 108), the ISP front-end logic 80 may perform one or moreimage processing operations, such as temporal filtering and/or binningcompensation filtering. The processed image data may then be provided tothe ISP pipe logic 82 (output signal 109) for additional processingprior to being displayed (e.g., on display device 28), or may be sent tothe memory (output signal 110). The ISP pipe logic 82 receives the“front-end” processed data, either directly form the ISP front-end logic80 or from the memory 108 (input signal 112), and may provide foradditional processing of the image data in the raw domain, as well as inthe RGB and YCbCr color spaces. Image data processed by the ISP pipelogic 82 may then be output (signal 114) to the display 28 for viewingby a user and/or may be further processed by a graphics engine or GPU.Additionally, output from the ISP pipe logic 82 may be sent to memory108 (signal 115) and the display 28 may read the image data from memory108 (signal 116), which may, in certain embodiments, be configured toimplement one or more frame buffers. Further, in some implementations,the output of the ISP pipe logic 82 may also be provided to acompression/decompression engine 118 (signal 117) for encoding/decodingthe image data. The encoded image data may be stored and then laterdecompressed prior to being displayed on the display 28 device (signal119). By way of example, the compression engine or “encoder” 118 may bea JPEG compression engine for encoding still images, or an H.264compression engine for encoding video images, or some combinationthereof, as well as a corresponding decompression engine for decodingthe image data. Additional information with regard to image processingoperations that may be provided in the ISP pipe logic 82 will bediscussed in greater detail below with regard to FIGS. 67-97. Also, itshould be noted that the ISP pipe logic 82 may also receive raw imagedata from the memory 108, as depicted by input signal 112.

Statistical data 102 determined by the ISP front-end logic 80 may beprovided to a control logic unit 84. The statistical data 102 mayinclude, for example, image sensor statistics relating to auto-exposure,auto-white balance, auto-focus, flicker detection, black levelcompensation (BLC), lens shading correction, and so forth. The controllogic 84 may include a processor and/or microcontroller configured toexecute one or more routines (e.g., firmware) that may be configured todetermine, based upon the received statistical data 102, controlparameters 104 for the imaging device 30, as well as control parameters106 for the ISP pipe processing logic 82. By way of example only, thecontrol parameters 104 may include sensor control parameters (e.g.,gains, integration time for exposure control), camera flash controlparameters, lens control parameters (e.g., focal length for focusing orzoom), or a combination of such parameters. The ISP control parameters106 may include gain levels and color correction matrix (CCM)coefficients for auto-white balance and color adjustment (e.g., duringRGB processing), as well as lens shading correction parameters which, asdiscussed below, may be determined based upon white point balanceparameters. In some embodiments, the control logic 84 may, in additionto analyzing statistics data 102, also analyze historical statistics,which may be stored on the electronic device 10 (e.g., in memory 18 orstorage 20).

Due to the generally complex design of the image processing circuitry 32shown herein, it may be beneficial to separate the discussion of the ISPfront-end logic 80 and the ISP pipe processing logic 82 into separatesections, as shown below. Particularly, FIGS. 8 to 66 of the presentapplication may relate to the discussion of various embodiments andaspects of the ISP front-end logic 80, while FIGS. 67 to 97 of thepresent application may relate to the discussion of various embodimentsand aspects of the ISP pipe processing logic 82.

The ISP Front-End Processing Logic

FIG. 8 is a more detailed block diagram showing functional logic blocksthat may be implemented in the ISP front-end logic 80, in accordancewith one embodiment. Depending on the configuration of the imagingdevice 30 and/or sensor interface 94, as discussed above in FIG. 7, rawimage data may be provided to the ISP front-end logic 80 by one or moreimage sensors 90. In the depicted embodiment, raw image data may beprovided to the ISP front-end logic 80 by a first image sensor 90 a(Sensor0) and a second image sensor 90 b (Sensor1). As will be discussedfurther below, each image sensor 90 a and 90 b may be configured toapply binning to full resolution image data in order to increasesignal-to-noise ratio of the image signal. For instance, a binningtechnique, such as 2×2 binning, may be applied which may interpolate a“binned” raw image pixel based upon four full-resolution image pixels ofthe same color. In one embodiment, this may result in there being fouraccumulated signal components associated with the binned pixel versus asingle noise component, thus improving signal-to-noise of the imagedata, but reducing overall resolution. Additionally, binning may alsoresult in an uneven or non-uniform spatial sampling of the image data,which may be corrected using binning compensation filtering, as will bediscussed in more detail below.

As shown, the image sensors 90 a and 90 b may provide the raw image dataas signals Sif0 and Sif1, respectively. Each of the image sensors 90 aand 90 b may be generally associated with the respective statisticsprocessing units 120 (StatsPipe0) and 122 (StatsPipe1), which may beconfigured to process image data for the determination of one or moresets of statistics (as indicated by signals Stats0 and Stats1),including statistics relating to auto-exposure, auto-white balance,auto-focus, flicker detection, black level compensation, and lensshading correction, and so forth. In certain embodiments, when only oneof the sensors 90 a or 90 b is actively acquiring image, the image datamay be sent to both StatsPipe0 and StatsPipe1 if additional statisticsare desired. For instance, to provide one example, if StatsPipe0 andStatsPipe1 are both available, StatsPipe0 may be utilized to collectstatistics for one color space (e.g., RGB), and StatsPipe1 may beutilized to collect statistics for another color space (e.g., YUV orYCbCr). That is, the statistics process units 120 and 122 may operate inparallel to collect multiple sets of statistics for each frame of theimage data acquired by the active sensor.

In the present embodiment, five asynchronous sources of data areprovided in the ISP front-end 80. These include: (1) a direct input froma sensor interface corresponding to Sensor0 (90 a) (referred to as Sif0or Sens0), (2) a direct input from a sensor interface corresponding toSensor1 (90 b) (referred to as Sif1 or Sens1), (3) Sensor0 data inputfrom the memory 108 (referred to as SifIn0 or Sens0DMA), which mayinclude a DMA interface, (4) Sensor1 data input from the memory 108(referred to as SifIn1 or Sens1DMA), and (5) a set of image data withframes from Sensor0 and Sensor1 data input retrieved from the memory 108(referred to as FeProcIn or ProcInDMA). The ISP front-end 80 may alsoinclude multiple destinations to which image data from the sources maybe routed, wherein each destination may be either a storage location inmemory (e.g., in 108), or a processing unit. For instance, in thepresent embodiment, the ISP front-end 80 includes six destinations: (1)Sif0DMA for receiving Sensor0 data in the memory 108, (2) Sif1DMA forreceiving Sensor1 data in the memory 108, (3) the first statisticsprocessing unit 120 (StatsPipe0), (4) the second statistics processingunit 122 (StatsPipe1), (5) the front-end pixel processing unit (FEProc)130, and (6) FeOut (or FEProcOut) to memory 108 or the ISP pipeline 82(discussed in further detail below). In one embodiment, the ISPfront-end 80 may be configured such that only certain destinations arevalid for a particular source, as shown in Table 1 below.

TABLE 1 Example of ISP Front-end valid destinations for each sourceSIf0- SIf1- DMA DMA StatsPipe0 StatsPipe1 FEProc FEOut Sens0 X X X X XSens1 X X X X X Sens0DMA X Sens1DMA X ProcInDMA X X

For instance, in accordance with Table 1, source Sens0 (sensor interfaceof Sensor0) may be configured to provide data to destinations SIf0DMA(signal 134), StatsPipe0 (signal 136), StatsPipe1 (signal 138), FEProc(signal 140), or FEOut (signal 142). With regard to FEOut, source datamay, in some instances, be provided to FEOut to bypass pixel processingby FEProc, such as for debugging or test purposes. Additionally, sourceSens1 (sensor interface of Sensor1) may be configured to provide data todestinations SIf1DMA (signal 144), StatsPipe0 (signal 146), StatsPipe1(signal 148), FEProc (signal 150), or FEOut (signal 152), sourceSens0DMA (Sensor0 data from memory 108) may be configured to providedata to StatsPipe0 (signal 154), source Sens1DMA (Sensor1 data frommemory 108) may be configured to provide data to StatsPipe1 (signal156), and source ProcInDMA (Sensor0 and Sensor1 data from memory 108)may be configured to provide data to FEProc (signal 158) and FEOut(signal 160).

It should be noted that the presently illustrated embodiment isconfigured such that Sens0DMA (Sensor0 frames from memory 108) andSens1DMA (Sensor1 frames from memory 108) are only provided toStatsPipe0 and StatesPipe1, respectively. This configuration allows theISP front-end 80 to retain a certain number of previous frames (e.g., 5frames) in memory. For example, due to a delay or lag between the time auser initiates a capture event (e.g., transitioning the image systemfrom a preview mode to a capture or a recording mode, or even by justturning on or initializing the image sensor) using the image sensor towhen an image scene is captured, not every frame that the user intendedto capture may be captured and processed in substantially real-time.Thus, by retaining a certain number of previous frames in memory 108(e.g., from a preview phase), these previous frames may be processedlater or alongside the frames actually captured in response to thecapture event, thus compensating for any such lag and providing a morecomplete set of image data.

With regard to the illustrated configuration of FIG. 8, it should benoted that the StatsPipe0 120 is configured to receive one of the inputs136 (from Sens0), 146 (from Sens1), and 154 (from Sens0DMA), asdetermined by a selection logic 124, such as a multiplexer. Similarly,selection logic 126 may select an input from the signals 138, 156, and148 to provide to StatsPipe1, and selection logic 132 may select aninput from the signals 140, 150, and 158 to provide to FEProc. Asmentioned above, the statistical data (Stats0 and Stats1) may beprovided to the control logic 84 for the determination of variouscontrol parameters that may be used to operate the imaging device 30and/or the ISP pipe processing logic 82. As can be appreciated, theselection logic blocks (120, 122, and 132) shown in FIG. 8 may beprovided by any suitable type of logic, such as a multiplexer thatselects one of multiple input signals in response to a control signal.

The pixel processing unit (FEProc) 130 may be configured to performvarious image processing operations on the raw image data on apixel-by-pixel basis. As shown, FEProc 130, as a destination processingunit, may receive image data from sources Sens0 (signal 140), Sens1(signal 150), or ProcInDMA (signal 158) by way of the selection logic132. FEProc 130 may also receive and output various signals (e.g., Rin,Hin, Hout, and Yout—which may represent motion history and luma dataused during temporal filtering) when performing the pixel processingoperations, which may include temporal filtering and binningcompensation filtering, as will be discussed further below. The output109 (FEProcOut) of the pixel processing unit 130 may then be forwardedto the ISP pipe logic 82, such as via one or more first-in-first-out(FIFO) queues, or may be sent to the memory 108.

Further, as shown in FIG. 8, the selection logic 132, in addition toreceiving the signals 140, 150, and 158, may further receive the signals159 and 161. The signal 159 may represented “pre-processed” raw imagedata from StatsPipe0, and the signal 161 may represent “pre-processed”raw image data from StatsPipe1. As will be discussed below, each of thestatistics processing units may apply one or more pre-processingoperations to the raw image data before collecting statistics. In oneembodiment, each of the statistics processing units may perform a degreeof defective pixel detection/correction, lens shading correction, blacklevel compensation, and inverse black level compensation. Thus, thesignals 159 and 161 may represent raw image data that has been processedusing the aforementioned pre-processing operations (as will be discussedin further detail below in FIG. 37). Thus, the selection logic 132 givesthe ISP front-end processing logic 80 the flexibility of providingeither un-pre-processed raw image data from the Sensor0 (signal 140) andSensor1 (signal 150) or pre-processed raw image data from StatsPipe0(signal 159) and StatsPipe1 (signal 161). Additionally, as shown byselection logic units 162 and 163, the ISP front-end processing logic 80also has the flexibility of writing either un-pre-processed raw imagedata from Sensor0 (signal 134) or Sensor1 (signal 144) to the memory108, or writing pre-processed raw image data from StatsPipe0 (signal159) or StatsPipe1 (signal 161) to the memory 108.

To control the operation of the ISP front-end logic 80, a front-endcontrol unit 164 is provided. The control unit 164 may be configured toinitialize and program control registers (referred to herein as “goregisters”) for configuring and starting the processing of an imageframe and to select an appropriate register bank(s) for updatingdouble-buffered data registers. In some embodiments, the control unit164 may also provide performance monitoring logic to log clock cycles,memory latency, and quality of service (QOS) information. Further, thecontrol unit 164 may also control dynamic clock gating, which may beused to disable clocks to one or more portions of the ISP front-end 0when there is not enough data in the input queue from an active sensor.

Using the “go registers” mentioned above, the control unit 164 may beable to control the updating of various parameters for each of theprocessing units (e.g., StatsPipe0, StatsPipe1, and FEProc) and mayinterface with the sensor interfaces to control the starting andstopping of the processing units. Generally each of the front-endprocessing units operates on a frame-by-frame basis. As discussed above(Table 1), the input to the processing units may be from the sensorinterface (Sens0 or Sens1) or from memory 108. Further, the processingunits may utilize various parameters and configuration data, which maybe stored in corresponding data registers. In one embodiment, the dataregisters associated with each processing unit or destination may begrouped into blocks forming a register bank group. In the embodiment ofFIG. 8, seven register bank groups may be defined in ISP Front-end:SIf0, SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut and ProcIn. Eachregister block address space is duplicated to provide two banks ofregisters. Only the registers that are double buffered are instantiatedin the second bank. If a register is not double buffered, the address inthe second bank may be mapped to the address of the same register in thefirst bank.

For registers that are double buffered, registers from one bank areactive and used by the processing units while the registers from theother bank are shadowed. The shadowed register may be updated by thecontrol unit 164 during the current frame interval while hardware isusing the active registers. The determination of which bank to use for aparticular processing unit at a particular frame may be specified by a“NextBk” (next bank) field in a go register corresponding to the sourceproviding the image data to the processing unit. Essentially, NextBk isa field that allows the control unit 164 to control which register bankbecomes active on a triggering event for the subsequent frame.

Before discussing the operation of the go registers in detail, FIG. 9provides a general method 166 for processing image data on aframe-by-frame basis in accordance with the present techniques.Beginning at step 168, the destination processing units targeted by adata source (e.g., Sens0, Sens1, Sens0DMA, Sens1DMA, or ProcInDMA) enteran idle state. This may indicate that processing for the current frameis completed and, therefore, the control unit 164 may prepare forprocessing the next frame. For instance, at step 170, programmableparameters for each destination processing unit are updated. This mayinclude, for example, updating the NextBk field in the go registercorresponding to the source, as well as updating any parameters in thedata registers corresponding to the destination units. Thereafter, atstep 172, a triggering event may place the destination units into a runstate. Further, as shown at step 174, each destination unit targeted bythe source completes its processing operations for the current frame,and the method 166 may subsequently return to step 168 for theprocessing of the next frame.

FIG. 10 depicts a block diagram view showing two banks of data registers176 and 178 that may be used by the various destination units of theISP-front end. For instance, Bank 0 (176) may include the data registers1-n (176 a-176 d), and Bank 1 (178) may include the data registers 1-n(178 a-178 d). As discussed above, the embodiment shown in FIG. 8 mayutilize a register bank (Bank 0) having seven register bank groups(e.g., SIf0, SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut and ProcIn).Thus, in such an embodiment, the register block address space of eachregister is duplicated to provide a second register bank (Bank 1).

FIG. 10 also illustrates go register 180 that may correspond to one ofthe sources. As shown, the go register 180 includes a “NextVld” field182 and the above-mentioned “NextBk” field 184. These fields may beprogrammed prior to starting the processing of the current frame.Particularly, NextVld may indicate the destination(s) to where data fromthe source is to be sent. As discussed above, NextBk may select acorresponding data register from either Bank0 or Bank1 for eachdestination targeted, as indicated by NextVld. Though not shown in FIG.10, the go register 180 may also include an arming bit, referred toherein as a “go bit,” which may be set to arm the go register. When atriggering event 192 for a current frame is detected, NextVld and NextBkmay be copied into a CurrVld field 188 and a CurrBk field 190 of acorresponding current or “active” register 186. In one embodiment, thecurrent register(s) 186 may be read-only registers that may set byhardware, while remaining inaccessible to software commands within theISP front-end 80.

As will be appreciated, for each ISP front-end source, a correspondinggo register may be provided. For the purposes of this disclosure, the goregisters corresponding to the above-discussed sources Sens0, Sens1,Sens0DMA, Sens1DMA, and ProcInDMA may be referred to as Sens0Go,Sens1Go, Sens0DMAGo, Sens1DMAGo and ProcInDMAGo, respectively. Asmentioned above, the control unit may utilize the go registers tocontrol the sequencing of frame processing within the ISP front end 80.Each go register contains a NextVld field and a NextBk field to indicatewhat destinations will be valid, and which register bank (0 or 1) willbe used, respectively, for the next frame. When the next frame'striggering event 192 occurs, the NextVld and NextBk fields are copied toa corresponding active read-only register 186 that indicates the currentvalid destinations and bank numbers, as shown above in FIG. 10. Eachsource may be configured to operate asynchronously and can send data toany of its valid destinations. Further, it should be understood that foreach destination, generally only one source may be active during acurrent frame.

With regard to the arming and triggering of the go register 180,asserting an arming bit or “go bit” in the go register 180 arms thecorresponding source with the associated NextVld and NextBk fields. Fortriggering, various modes are available depending on whether the sourceinput data is read from memory (e.g., Sens0DMA, Sens1DMA or ProcInDMA),or whether the source input data is from a sensor interface (e.g., Sens0or Sens1). For instance, if the input is from memory 108, the arming ofthe go bit itself may serve as the triggering event, since the controlunit 164 has control over when data is read from the memory 108. If theimage frames are being input by the sensor interface, then triggeringevent may depend on the timing at which the corresponding go register isarmed relative to when data from the sensor interface is received. Inaccordance with the present embodiment, three different techniques fortriggering timing from a sensor interface input are shown in FIGS.11-13.

Referring first to FIG. 11, a first scenario is illustrated in whichtriggering occurs once all destinations targeted by the sourcetransition from a busy or run state to an idle state. Here, a datasignal VVALID (196) represents an image data signal from a source. Thepulse 198 represents a current frame of image data, the pulse 202represents the next frame of image data, and the interval 200 representsa vertical blanking interval (VBLANK) 200 (e.g., represents the timedifferential between the last line of the current frame 198 and the nextframe 202). The time differential between the rising edge and fallingedge of the pulse 198 represents a frame interval 201. Thus, in FIG. 11,the source may be configured to trigger when all targeted destinationshave finished processing operations on the current frame 198 andtransition to an idle state. In this scenario, the source is armed(e.g., by setting the arming or “go” bit) before the destinationscomplete processing so that the source can trigger and initiateprocessing of the next frame 202 as soon as the targeted destinations goidle. During the vertical blanking interval 200 the processing units maybe set up and configured for the next frame 202 using the register banksspecified by the go register corresponding to the source before thesensor input data arrives. By way of example only, read buffers used byFEProc 130 may be filled before the next frame 202 arrives. In thiscase, shadowed registers corresponding to the active register banks maybe updated after the triggering event, thus allowing for a full frameinterval to setup the double-buffered registers for the next frame(e.g., after frame 202).

FIG. 12 illustrates a second scenario in which the source is triggeredby arming the go bit in the go register corresponding to the source.Under this “trigger-on-go” configuration, the destination units targetedby the source are already idle and the arming of the go bit is thetriggering event. This triggering mode may be utilized for registersthat are not double-buffered and, therefore, are updated during verticalblanking (e.g., as opposed to updating a double-buffered shadow registerduring the frame interval 201).

FIG. 13 illustrates a third triggering mode in which the source istriggered upon detecting the start of the next frame, i.e., a risingVSYNC. However, it should be noted that in this mode, if the go registeris armed (by setting the go bit) after the next frame 202 has alreadystarted processing, the source will use the target destinations andregister banks corresponding to the previous frame, since the CurrVldand CurrBk fields are not updated before the destination startprocessing. This leaves no vertical blanking interval for setting up thedestination processing units and may potentially result in droppedframes, particularly when operating in a dual sensor mode. It should benoted, however, that this mode may nonetheless result in accurateoperation if the image processing circuitry 32 is operating in a singlesensor mode that uses the same register banks for each frame (e.g., thedestination (NextVld) and register banks (NextBk) do not change).

Referring now to FIG. 14, a control register (or “go register”) 180 isillustrated in more detail. The go register 180 includes the arming “go”bit 204, as well as the NextVld field 182 and the NextBk field 184. Asdiscussed above, each source (e.g., Sens0, Sens1, Sens0DMA, Sens1DMA, orProcInDMA) of the ISP front-end 80 may have a corresponding go register180. In one embodiment, the go bit 204 may be a single-bit field, andthe go register 180 may be armed by setting the go bit 204 to 1. TheNextVld field 182 may contain a number of bits corresponding to thenumber of destinations in the ISP front-end 80. For instance, in theembodiment shown in FIG. 8, the ISP front-end includes six destinations:Sif0DMA, Sif1DMA, StatsPipe0, StatsPipe1, FEProc, and FEOut. Thus, thego register 180 may include six bits in the NextVld field 182, with onebit corresponding to each destination, and wherein targeted destinationsare set to 1. Similarly, the NextBk field 182 may contain a number ofbits corresponding to the number of data registers in the ISP front-end80. For instance, as discussed above, the embodiment of the ISPfront-end 80 shown in FIG. 8 may include seven data registers: SIf0,SIf1, StatsPipe0, StatsPipe1, ProcPipe, FEOut and ProcIn. Accordingly,the NextBk field 184 may include seven bits, with one bit correspondingto each data register, and wherein data registers corresponding to Bank0 and 1 are selected by setting their respective bit values to 0 or 1,respectively. Thus, using the go register 180, the source, upontriggering, knows precisely which destination units are to receive framedata, and which register banks are to be used for configuring thetargeted destination units.

Additionally, due to the dual sensor configuration supported by the ISPcircuitry 32, the ISP front-end may operate in a single sensorconfiguration mode (e.g., only one sensor is acquiring data) and a dualsensor configuration mode (e.g., both sensors are acquiring data). In atypical single sensor configuration, input data from a sensor interface,such as Sens0, is sent to StatsPipe0 (for statistics processing) andFEProc (for pixel processing). In addition, sensor frames may also besent to memory (SIf0DMA) for future processing, as discussed above.

An example of how the NextVld fields corresponding to each source of theISP front-end 80 may be configured when operating in a single sensormode is depicted below in Table 2.

TABLE 2 NextVld per source example: Single sensor mode SIf0 SIf1- DMADMA StatsPipe0 StatsPipe1 FEProc FEOut Sens0Go 1 X 1 0 1 0 Sens1Go X 0 00 0 0 Sens0DMAGo X X 0 X X X Sens1DMAGo X X X 0 X X ProcInDMAGo X X X X0 0As discussed above with reference to Table 1, the ISP front-end 80 maybe configured such that only certain destinations are valid for aparticular source. Thus, the destinations in Table 2 marked with “X” areintended to indicate that the ISP front-end 80 is not configured toallow a particular source to send frame data to that destination. Forsuch destinations, the bits of the NextVld field of the particularsource corresponding to that destination may always be 0. It should beunderstood, however, that this is merely one embodiment and, indeed, inother embodiments, the ISP front-end 80 may be configured such that eachsource is capable of targeting each available destination unit.

The configuration shown above in Table 2 represents a single sensor modein which only Sensor0 is providing frame data. For instance, the Sens0Goregister indicates destinations as being SIf0DMA, StatsPipe0, andFEProc. Thus, when triggered, each frame of the Sensor0 image data, issent to these three destinations. As discussed above, SIf0DMA may storeframes in memory 108 for later processing, StatsPipe0 applies statisticsprocessing to determine various statistic data points, and FEProcprocesses the frame using, for example, temporal filtering and binningcompensation filtering. Further, in some configurations where additionalstatistics are desired (e.g., statistics in different color spaces),StatsPipe1 may also be enabled (corresponding NextVld set to 1) duringthe single sensor mode. In such embodiments, the Sensor0 frame data issent to both StatsPipe0 and StatsPipe1. Further, as shown in the presentembodiment, only a single sensor interface (e.g., Sens0 or alternativelySen0) is the only active source during the single sensor mode.

With this in mind, FIG. 15 provides a flow chart depicting a method 206for processing frame data in the ISP front-end 80 when only a singlesensor is active (e.g., Sensor 0). While the method 206 illustrates inparticular the processing of Sensor0 frame data by FEProc 130 as anexample, it should be understood that this process may be applied to anyother source and corresponding destination unit in the ISP front-end 80.Beginning at step 208, Sensor0 begins acquiring image data and sendingthe captured frames to the ISP front-end 80. The control unit 164 mayinitialize programming of the go register corresponding to Sens0 (theSensor0 interface) to determine target destinations (including FEProc)and what bank registers to use, as shown at step 210. Thereafter,decision logic 212 determines whether a source triggering event hasoccurred. As discussed above, frame data input from a sensor interfacemay utilize different triggering modes (FIGS. 11-13). If a trigger eventis not detected, the process 206 continues to wait for the trigger. Oncetriggering occurs, the next frame becomes the current frame and is sentto FEProc (and other target destinations) for processing at step 214.FEProc may be configured using data parameters based on a correspondingdata register (ProcPipe) specified in the NextBk field of the Sens0Goregister. After processing of the current frame is completed at step216, the method 206 may return to step 210, at which the Sens0Goregister is programmed for the next frame.

When both Sensor0 and Sensor1 of the ISP front-end 80 are both active,statistics processing remains generally straightforward, since eachsensor input may be processed by a respective statistics block,StatsPipe0 and StatsPipe1. However, because the illustrated embodimentof the ISP front-end 80 provides only a single pixel processing unit(FEProc), FEProc may be configured to alternate between processingframes corresponding to Sensor0 input data and frames corresponding toSensor1 input data. As will be appreciated, the image frames are readfrom FEProc in the illustrated embodiment to avoid a condition in whichimage data from one sensor is processed in real-time while image datafrom the other sensor is not processed in real-time. For instance, asshown in Table 3 below, which depicts one possible configuration ofNextVld fields in the go registers for each source when the ISP-frontend 80 is operating in a dual sensor mode, input data from each sensoris sent to memory (SIf0DMA and SIf1DMA) and to the correspondingstatistics processing unit (StatsPipe0 and StatsPipe1).

TABLE 3 NextVld per source example: Dual sensor mode SIf0- SIf1- DMA DMAStatsPipe0 StatsPipe1 FEProc FEOut Sens0Go 1 X 1 0 0 0 Sens1Go X 1 0 1 00 Sens0DMAGo X X 0 X X X Sens1DMAGo X X X 0 X X ProcInDMAGo X X X X 1 0

The sensor frames in memory are sent to FEProc from the ProcInDMAsource, such that they alternate between Sensor0 and Sensor1 at a ratebased on their corresponding frame rates. For instance, if Sensor0 andSensor1 are both acquiring image data at a rate of 30 frames per second(fps), then their sensor frames may be interleaved in a 1-to-1 manner.If Sensor0 (30 fps) is acquiring image data at a rate twice that ofSensor1 (15 fps), then the interleaving may be 2-to-1, for example. Thatis, two frames of Sensor0 data are read out of memory for every oneframe of Sensor1 data.

With this in mind, FIG. 16 depicts a method 220 for processing framedata in the ISP front-end 80 having two sensors acquiring image datasimultaneously. At step 222, both Sensor0 and Sensor1 begin acquiringimage frames. As will be appreciated, Sensor0 and Sensor1 may acquirethe image frames using different frame rates, resolutions, and so forth.At step 224, the acquired frames from Sensor0 and Sensor1 written tomemory 108 (e.g., using SIf0DMA and SIf1DMA destinations). Next, sourceProcInDMA reads the frame data from the memory 108 in an alternatingmanner, as indicated at step 226. As discussed, frames may alternatebetween Sensor0 data and Sensor1 data depending on frame rate at whichthe data is acquired. At step 228, the next frame from ProcInDMA isacquired. Thereafter, at step 230, the NextVld and NextBk fields of thego register corresponding to the source, here ProcInDMA, is programmeddepending on whether the next frame is Sensor0 or Sensor1 data.Thereafter, decision logic 232 determines whether a source triggeringevent has occurred. As discussed above, data input from memory may betriggered by arming the go bit (e.g., “trigger-on-go” mode). Thus,triggering may occur once the go bit of the go register is set to 1.Once triggering occurs, the next frame becomes the current frame and issent to FEProc for processing at step 234. As discussed above, FEProcmay be configured using data parameters based on a corresponding dataregister (ProcPipe) specified in the NextBk field of the ProcInDMAGoregister. After processing of the current frame is completed at step236, the method 220 may return to step 228 and continue.

A further operational event that the ISP front-end 80 is configured tohandle is a configuration change during image processing. For instance,such an event may occur when the ISP front-end 80 transitions from asingle sensor configuration to a dual sensor configuration, orvice-versa. As discussed above, the NextVld fields for certain sourcesmay be different depending on whether one or both image sensors areactive. Thus, when the sensor configuration is changed, the ISPfront-end control unit 164 may release all destination units before theyare targeted by a new source. This may avoid invalid configurations(e.g., assigning multiple sources to one destination). In oneembodiment, the release of the destination units may be accomplished bysetting the NextVld fields of all the go registers to 0, thus disablingall destinations, and arming the go bit. After the destination units arereleased, the go registers may be reconfigured depending on the currentsensor mode, and image processing may continue.

A method 240 for switching between single and dual sensor configurationsis shown in FIG. 17, in accordance with one embodiment. Beginning atstep 242, a next frame of image data from a particular source of the ISPfront-end 80 is identified. At step 244, the target destinations(NextVld) are programmed into the go register corresponding to thesource. Next, at step 246, depending on the target destinations, NextBkis programmed to point to the correct data registers associated with thetarget destinations. Thereafter, decision logic 248 determines whether asource triggering event has occurred. Once triggering occurs, the nextframe is sent to the destination units specified by NextVld andprocessed by the destination units using the corresponding dataregisters specified by NextBk, as shown at step 250. The processingcontinues until step 252, at which the processing of the current frameis completed.

Subsequently, decision logic 254 determines whether there is a change inthe target destinations for the source. As discussed above, NextVldsettings of the go registers corresponding to Sens0 and Sens1 may varydepending on whether one sensor or two sensors are active. For instance,referring to Table 2, if only Sensor0 is active, Sensor0 data is sent toSIf0DMA, StatsPipe0, and FEProc. However, referring to Table 3, if bothSensor0 and Sensor1 are active, then Sensor0 data is not sent directlyFEProc. Instead, as mentioned above, Sensor0 and Sensor1 data is writtento memory 108 and is read out to FEProc in an alternating manner bysource ProcInDMA. Thus, if no target destination change is detected atdecision logic 254, the control unit 164 deduces that the sensorconfiguration has not changed, and the method 240 returns to step 246,whereat the NextBk field of the source go register is programmed topoint to the correct data registers for the next frame, and continues.

If, however, at decision logic 254, a destination change is detected,then the control unit 164 determines that a sensor configuration changehas occurred. For instance, this could represent switching from singlesensor mode to dual sensor mode, or shutting off the sensors altogether.Accordingly, the method 240 continues to step 256, at which all bits ofthe NextVld fields for all go registers are set to 0, thus effectivelydisabling the sending of frames to any destination on the next trigger.Then, at decision logic 258, a determination is made as to whether alldestination units have transition to an idle state. If not, the method240 waits at decision logic 258 until all destinations units havecompleted their current operations. Next, at decision logic 260, adetermination is made as to whether image processing is to continue. Forinstance, if the destination change represented the deactivation of bothSensor0 and Sensor1, then image processing ends at step 262. However, ifit is determined that image processing is to continue, then the method240 returns to step 244 and the NextVld fields of the go registers areprogrammed in accordance with the current operation mode (e.g., singlesensor or dual sensor). As shown here, the steps 254-262 for clearingthe go registers and destination fields may collectively be referred toby reference number 264.

Next, FIG. 18 shows a further embodiment by way of the flow chart(method 265) that provides for another dual sensor mode of operation.The method 265 depicts a condition in which one sensor (e.g., Sensor0)is actively acquiring image data and sending the image frames to FEProc130 for processing, while also sending the image frames to StatsPipe0and/or memory 108 (Sif0DMA), while the other sensor (e.g., Sensor1) isinactive (e.g., turned off), as shown at step 266. Decision logic 268then detects for a condition in which Sensor1 will become active on thenext frame to send image data to FEProc. If this condition is not met,then the method 265 returns to step 266. However, if this condition ismet, then the method 265 proceeds by performing action 264 (collectivelysteps 254-262 of FIG. 17), whereby the destination fields of the sourcesare cleared and reconfigured at step 264. For instance, at step 264, theNextVld field of the go register associated with Sensor1 may beprogrammed to specify FEProc as a destination, as well as StatsPipe1and/or memory (Sif1DMA), while the NextVld field of the go registerassociated with Sensor0 may be programmed to clear FEProc as adestination. In this embodiment, although frames captured by Sensor0 arenot sent to FEProc on the next frame, Sensor0 may remain active andcontinue to send its image frames to StatsPipe0, as shown at step 270,while Sensor1 captures and sends data to FEProc for processing at step272. Thus, both sensors, Sensor0 and Sensor1 may continue to operate inthis “dual sensor” mode, although only image frames from one sensor aresent to FEProc for processing. For the purposes of this example, asensor sending frames to FEProc for processing may be referred to as an“active sensor,” a sensor that is not sending frame FEProc but is stillsending data to the statistics processing units may be referred to as a“semi-active sensor,” and a sensor that is not acquiring data at all maybe referred to as an “inactive sensor.”

One benefit of the foregoing technique is that the because statisticscontinue to be acquired for the semi-active sensor (Sensor0), the nexttime the semi-active sensor transitions to an active state and thecurrent active sensor (Sensor1) transitions to a semi-active or inactivestate, the semi-active sensor may begin acquiring data within one frame,since color balance and exposure parameters may already be available dueto the continued collection of image statistics. This technique may bereferred to as “hot switching” of the image sensors, and avoidsdrawbacks associated with “cold starts” of the image sensors (e.g.,starting with no statistics information available). Further, to savepower, since each source is asynchronous (as mentioned above), thesemi-active sensor may operate at a reduced clock and/or frame rateduring the semi-active period.

Before continuing with a more detailed description of the statisticsprocessing and pixel processing operations depicted in the ISP front-endlogic 80 of FIG. 8, it is believed that a brief introduction regardingthe definitions of various ISP frame regions will help to facilitate abetter understanding of the present subject matter. With this in mind,various frame regions that may be defined within an image source frameare illustrated in FIG. 19. The format for a source frame provided tothe image processing circuitry 32 may use either the tiled or linearaddressing modes discussed above, as may utilize pixel formats in 8, 10,12, or 14-bit precision. The image source frame 274, as shown in FIG.19, may include a sensor frame region 276, a raw frame region 276, andan active region 278. The sensor frame 276 is generally the maximumframe size that the image sensor 90 can provide to the image processingcircuitry 32. The raw frame region 278 may be defined as the region ofthe sensor frame 276 that is sent to the ISP front-end processing logic80. The active region 280 may be defined as a portion of the sourceframe 274, typically within the raw frame region 278, on whichprocessing is performed for a particular image processing operation. Inaccordance with embodiments of the present technique, that active region280 may be the same or may be different for different image processingoperations.

In accordance with aspects of the present technique, the ISP front-endlogic 80 only receives the raw frame 278. Thus, for the purposes of thepresent discussion, the global frame size for the ISP front-endprocessing logic 80 may be assumed as the raw frame size, as determinedby the width 282 and height 284. In some embodiments, the offset fromthe boundaries of the sensor frame 276 to the raw frame 278 may bedetermined and/or maintained by the control logic 84. For instance, thecontrol logic 84 may be include firmware that may determine the rawframe region 278 based upon input parameters, such as the x-offset 286and the y-offset 288, that are specified relative to the sensor frame276. Further, in some cases, a processing unit within the ISP front-endlogic 80 or the ISP pipe logic 82 may have a defined active region, suchthat pixels in the raw frame but outside the active region 280 will notbe processed, i.e., left unchanged. For instance, an active region 280for a particular processing unit having a width 290 and height 292 maybe defined based upon an x-offset 294 and y-offset 296 relative to theraw frame 278. Further, where an active region is not specificallydefined, one embodiment of the image processing circuitry 32 may assumethat the active region 280 is the same as the raw frame 278 (e.g.,x-offset 294 and y-offset 296 are both equal to 0). Thus, for thepurposes of image processing operations performed on the image data,boundary conditions may be defined with respect to the boundaries of theraw frame 278 or active region 280.

Keeping these points in mind and referring to FIG. 20, a more detailedview of the ISP front-end pixel processing logic 130 (previouslydiscussed in FIG. 8) is illustrated, in accordance with an embodiment ofthe present technique. As shown, the ISP front-end pixel processinglogic 130 includes a temporal filter 298 and a binning compensationfilter 300. The temporal filter 298 may receive one of the input imagesignals Sif0, Sif1, FEProcIn, or pre-processed image signals (e.g., 159,161) and may operate on the raw pixel data before any additionalprocessing is performed. For example, the temporal filter 298 mayinitially process the image data to reduce noise by averaging imageframes in the temporal direction. The binning compensation filter 300,which is discussed in more detail below, may apply scaling andre-sampling on binned raw image data from an image sensor (e.g., 90 a,90 b) to maintain an even spatial distribution of the image pixels.

The temporal filter 298 may be pixel-adaptive based upon motion andbrightness characteristics. For instance, when pixel motion is high, thefiltering strength may be reduced in order to avoid the appearance of“trailing” or “ghosting artifacts” in the resulting processed image,whereas the filtering strength may be increased when little or no motionis detected. Additionally, the filtering strength may also be adjustedbased upon brightness data (e.g., “luma”). For instance, as imagebrightness increases, filtering artifacts may become more noticeable tothe human eye. Thus, the filtering strength may be further reduced whena pixel has a high level of brightness.

In applying temporal filtering, the temporal filter 298 may receivereference pixel data (Rin) and motion history input data (Hin), whichmay be from a previous filtered or original frame. Using theseparameters, the temporal filter 298 may provide motion history outputdata (Hout) and filtered pixel output (Yout). The filtered pixel outputYout is then passed to the binning compensation filter 300, which may beconfigured to perform one or more scaling operations on the filteredpixel output data Yout to produce the output signal FEProcOut. Theprocessed pixel data FEProcOut may then be forwarded to the ISP pipeprocessing logic 82, as discussed above.

Referring to FIG. 21, a process diagram depicting a temporal filteringprocess 302 that may be performed by the temporal filter shown in FIG.20 is illustrated, in accordance with a first embodiment. The temporalfilter 298 may include a 2-tap filter, wherein the filter coefficientsare adjusted adaptively on a per pixel basis based at least partiallyupon motion and brightness data. For instance, input pixels x(t), withthe variable “t” denoting a temporal value, may be compared to referencepixels r(t−1) in a previously filtered frame or a previous originalframe to generate a motion index lookup in a motion history table (M)304 that may contain filter coefficients. Additionally, based uponmotion history input data h(t−1), a motion history output h(t)corresponding to the current input pixel x(t) may be determined.

The motion history output h(t) and a filter coefficient, K, may bedetermined based upon a motion delta d(j,i,t), wherein (j,i) representcoordinates of the spatial location of a current pixel x(j,i,t). Themotion delta d(j,i,t) may be computed by determining the maximum ofthree absolute deltas between original and reference pixels for threehorizontally collocated pixels of the same color. For instance,referring briefly to FIG. 22, the spatial locations of three collocatedreference pixels 308, 309, and 310 that corresponding to original inputpixels 312, 313, and 314 are illustrated. In one embodiment, the motiondelta may be calculated based on these original and reference pixelsusing formula below:

d(j,i,t)=max 3[abs(x(j,i−2,t)−r(j,i−2,t−1)),

(abs(x(j,i,t)−r(j,i,t−1)),

(abs(x(j,i+2,t)−r(j,i+2,t−1))]  (1a)

A flow chart depicting this technique for determining the motion deltavalue is illustrated further below in FIG. 24. Further, it should beunderstood that the technique for calculating the motion delta value, asshown above in Equation 1a (and below in FIG. 24), is only intended toprovide one embodiment for determining a motion delta value.

In other embodiments, an array of same-colored pixels could be evaluatedto determine a motion delta value. For instance, in addition to thethree pixels referenced in Equation 1a, one embodiment for determiningmotion delta values may include also evaluating the absolute deltasbetween same colored pixels from two rows above (e.g., j−2; assuming aBayer pattern) the reference pixels 312, 313, and 314 and theircorresponding collocated pixels, and two rows below (e.g., j+2; assuminga Bayer pattern) the reference pixels 312, 313, and 314 and theircorresponding collocated pixels. For instance, in one embodiment, themotion delta value may be expressed as follows:

d(j,i,t)=max 9[abs(x(j,i−2,t)−r(j,i−2,t−1)),

(abs(x(j,i,t)−r(j,i,t−1)),

(abs(x(j,i+2,t)−r(j,i+2,t−1)),

(abs(x(j−2,i−2,t)−r(j−2,i−2,t−1)),

(abs(x(j−2,i,t)−r(j−2,i,t−1)),

(abs(x(j−2,i+2,t)−r(j−2,i+2,t−1)),

(abs(x(j+2,i−2,t)−r(j+2,i−2,t−1))

(abs(x(j+2,i,t)−r(j+2,i,t−1)),

(abs(x(j+2,i+2,t)−r(j+2,i+2,t−1))]  (1b)

Thus, in the embodiment depicted by Equation 1b, the motion delta valuemay be determined by comparing the absolute delta between a 3×3 array ofsame-colored pixels, with the current pixel (313) being located at thecenter of the 3×3 array (e.g., really a 5×5 array for Bayer colorpatterns if pixels of different colors are counted). It should beappreciated, that any suitable two-dimensional array of same-coloredpixels (e.g., including arrays having all pixels in the same row (e.g.,Equation 1a) or arrays having all pixels in the same column) with thecurrent pixel (e.g., 313) being located at the center of the array couldbe analyzed to determine a motion delta value. Further, while the motiondelta value could be determined as the maximum of the absolute deltas(e.g., as shown in Equations 1a and 1b), in other embodiments, themotion delta value could also be selected as the mean or median of theabsolute deltas. Additionally, the foregoing techniques may also beapplied to other types of color filter arrays (e.g., RGBW, CYGM, etc.),and is not intended to be exclusive to Bayer patterns.

Referring back to FIG. 21, once the motion delta value is determined, amotion index lookup that may be used to selected the filter coefficientK from the motion table (M) 304 may be calculated by summing the motiondelta d(t) for the current pixel (e.g., at spatial location (j,i)) withthe motion history input h(t−1). For instance, the filter coefficient Kmay be determined as follows:

K=M[d(j,i,t)+h(j,i,t−1)]  (2a)

Additionally, the motion history output h(t) may be determined using thefollowing formula:

h(j,i,t)=d(j,i,t)+(1−K)×h(j,i,t−1)  (3a)

Next, the brightness of the current input pixel x(t) may be used togenerate a luma index lookup in a luma table (L) 306. In one embodiment,the luma table may contain attenuation factors that may be between 0 and1, and may be selected based upon the luma index. A second filtercoefficient, K′, may be calculated by multiplying the first filtercoefficient K by the luma attenuation factor, as shown in the followingequation:

K′=K×L[x(j,i,t)]  (4a)

The determined value for K′ may then be used as the filteringcoefficient for the temporal filter 298. As discussed above, thetemporal filter 298 may be a 2-tap filter. Additionally, the temporalfilter 298 may be configured as an infinite impulse response (IIR)filter using previous filtered frame or as a finite impulse response(FIR) filter using previous original frame. The temporal filter 298 maycompute the filtered output pixel y(t) (Yout) using the current inputpixel x(t), the reference pixel r(t−1), and the filter coefficient K′using the following formula:

y(j,i,t)=r(j,i,t−1)+K′(x(j,i,t)−r(j,i,t−1))  (5a)

As discussed above, the temporal filtering process 302 shown in FIG. 21may be performed on a pixel-by-pixel basis. In one embodiment, the samemotion table M and luma table L may be used for all color components(e.g., R, G, and B). Additionally, some embodiments may provide a bypassmechanism, in which temporal filtering may be bypassed, such as inresponse to a control signal from the control logic 84. Further, as willbe discussed below with respect to FIGS. 26 and 27, one embodiment ofthe temporal filter 298 may utilize separate motion and luma tables foreach color component of the image data.

The embodiment of the temporal filtering technique described withreference to FIGS. 21 and 22 may be better understood in view of FIG.23, which depicts a flow chart illustrating a method 315, in accordancewith the above-described embodiment. The method 315 begins at step 316,at which a current pixel x(t) located at spatial location (j,i) of acurrent frame of image data is received by the temporal filtering system302. At step 317, a motion delta value d(t) is determined for thecurrent pixel x(t) based at least partially upon one or more collocatedreference pixels (e.g., r(t−1)) from a previous frame of the image data(e.g., the image frame immediately preceding the current frame). Atechnique for determining a motion delta value d(t) at step 317 isfurther explained below with reference to FIG. 24, and may be performedin accordance with Equation 1a, as shown above.

Once the motion delta value d(t) from step 317 is obtained, a motiontable lookup index may be determined using the motion delta value d(t)and a motion history input value h(t−1) corresponding to the spatiallocation (j,i) from the previous frame, as shown in step 318.Additionally, though not shown, a motion history value h(t)corresponding to the current pixel x(t) may also be determined at step318 once the motion delta value d(t) is known, for example, by usingEquation 3a shown above. Thereafter, at step 319, a first filtercoefficient K may be selected from a motion table 304 using the motiontable lookup index from step 318. The determination of the motion tablelookup index and the selection of the first filter coefficient K fromthe motion table may be performed in accordance with Equation 2a, asshown above.

Next, at step 320, an attenuation factor may be selected from a lumatable 306. For instance, the luma table 306 may contain attenuationfactors ranging from between approximately 0 and 1, and the attenuationfactor may be selected from the luma table 306 using the value of thecurrent pixel x(t) as a lookup index. Once the attenuation factor isselected, a second filter coefficient K′ may be determined at step 321using the selected attenuation factor and the first filter coefficient K(from step 319), as shown in Equation 4a above. Then, at step 322, atemporally filtered output value y(t) corresponding to the current inputpixel x(t) is determined based upon the second filter coefficient K′(from step 320), the value of the collocated reference pixel r(t−1), andthe value of the input pixel x(t). For instance, in one embodiment, theoutput value y(t) may be determined in accordance with Equation 5a, asshown above.

Referring to FIG. 24, the step 317 for determining the motion deltavalue d(t) from the method 315 is illustrated in more detail inaccordance with one embodiment. In particular, the determination of themotion delta value d(t) may generally correspond to the operationdepicted above in accordance with Equation 1a. As shown, the step 317may include the sub-steps 324-327. Beginning at sub-step 324, a set ofthree horizontally adjacent pixels having the same color value as thecurrent input pixel x(t) are identified. By way of example, inaccordance with the embodiment shown in FIG. 22 the image data mayinclude Bayer image data, and the three horizontally adjacent pixels mayinclude the current input pixel x(t) (313), a second pixel 312 of thesame color to the left of the current input pixel 313, and a third pixelof the same color to the right of the current input pixel 313.

Next, at sub-step 325, three collocated reference pixels 308, 309, and310 from the previous frame corresponding to the selected set of threehorizontally adjacent pixels 312, 313, and 314 are identified. Using theselected pixels 312, 313, and 314 and the three collocated referencepixels 308, 309, and 310, the absolute values of the differences betweeneach of the three selected pixels 312, 313, and 314 and theircorresponding collocated reference pixels 308, 309, and 310,respectively, are determined at sub-step 326. Subsequently, at sub-step327, the maximum of the three differences from sub-step 326 is selectedas the motion delta value d(t) for the current input pixel x(t). Asdiscussed above, FIG. 24, which illustrates the motion delta valuecalculation technique shown in Equation 1a, is only intended to provideone embodiment. Indeed, as discussed above, any suitable two-dimensionalarray of same-colored pixels with the current pixel being centered inthe array may be used to determine a motion delta value (e.g., Equation1b).

Another embodiment of a technique for applying temporal filtering toimage data is further depicted in FIG. 25. For instance, since signal tonoise ratios for different color components of the image data may bedifferent, a gain may be applied to the current pixel, such that thecurrent pixel is gained before selecting motion and luma values from themotion table 304 and luma table 306. By applying a respective gain thatis color dependent, signal to noise ratio may be more consistent amongthe different color components. By way of example only, in animplementation that uses raw Bayer image data, the red and blue colorchannels may generally be more sensitive compared to the green (Gr andGb) color channels. Thus, by applying an appropriate color-dependentgain to each processed pixel, the signal to noise variation between eachcolor component may be generally reduced, thereby reducing, among otherthings, ghosting artifacts, as well as consistency across differentcolors after auto-white balance gains.

With this in mind, FIG. 25 provides a flow chart depicting a method 328for applying temporal filtering to image data received by the front-endprocessing unit 130 in accordance with such an embodiment. Beginning atstep 329, a current pixel x(t) located at spatial location (j,i) of acurrent frame of image data is received by the temporal filtering system302. At step 330, a motion delta value d(t) is determined for thecurrent pixel x(t) based at least partially upon one or more collocatedreference pixels (e.g., r(t−1)) from a previous frame of the image data(e.g., the image frame immediately preceding the current frame). Thestep 330 may be similar to the step 317 of FIG. 23, and may utilize theoperation represented in Equation 1 above.

Next, at step 331, a motion table lookup index may be determined usingthe motion delta value d(t), a motion history input value h(t−1)corresponding to the spatial location (j,i) from the previous frame(e.g., corresponding to the collocated reference pixel r(t−1)), and again associated with the color of the current pixel. Thereafter, at step332, a first filter coefficient K may be selected from the motion table304 using the motion table lookup index determined at step 331. By wayof example only, in one embodiment, the filter coefficient K and themotion table lookup index may be determined as follows:

K=M[gain[c]×(d(j,i,t)+h(j,i,t−1))],  (2b)

wherein M represents the motion table, and wherein the gain[c]corresponds to a gain associated with the color of the current pixel.Additionally, though not shown in FIG. 25, it should be understood thata motion history output value h(t) for the current pixel may also bedetermined and may be used to apply temporal filtering to a collocatedpixel of a subsequent image frame (e.g., the next frame). In the presentembodiment, the motion history output h(t) for the current pixel x(t)may be determined using the following formula:

h(j,i,t)=d(j,i,t)+K[h(j,i,t−1)−d(j,i,t)]  (3b)

Next, at step 333, an attenuation factor may be selected from the lumatable 306 using a luma table lookup index determined based upon the gain(gain[c]) associated with the color of the current pixel x(t). Asdiscussed above, the attenuation factors stored in the luma table mayhave a range from approximately 0 to 1. Thereafter, at step 334, asecond filter coefficient K′ may be calculated based upon theattenuation factor (from step 333) and the first filter coefficient K(from step 332). By way of example only, in one embodiment, the secondfilter coefficient K′ and the luma table lookup index may be determinedas follows:

K′=K×L[gain[c]×x(j,i,t)]  (4b)

Next, at step 335, a temporally filtered output value y(t) correspondingto the current input pixel x(t) is determined based upon the secondfilter coefficient K′ (from step 334), the value of the collocatedreference pixel r(t−1), and the value of the input pixel x(t). Forinstance, in one embodiment, the output value y(t) may be determined asfollows:

y(j,i,t)=x(j,i,t)+K′(r(j,i,t−1)−x(j,i,t))  (5b)

Continuing to FIG. 26, a further embodiment of the temporal filteringprocess 336 is depicted. Here, the temporal filtering process 336 may beaccomplished in a manner similar to the embodiment discussed in FIG. 25,except that instead of applying a color-dependent gain (e.g., gain[c])to each input pixel and using shared motion and luma tables, separatemotion and luma tables are provided for each color components. Forinstance, as shown in FIG. 26, the motion tables 304 may include amotion table 304 a corresponding to a first color, a motion table 304 bcorresponding to a second color, and a motion table 304 c correspondingto an nth color, wherein n depends on the number of colors present inthe raw image data. Similarly, the luma tables 306 may include a lumatable 306 a corresponding to the first color, a luma table 306 bcorresponding to the second color, and the motion table 304 ccorresponding to the nth color. Thus, in an embodiment where the rawimage data is Bayer image data, three motion and luma tables may beprovided for each of the red, blue, and green color components. Asdiscussed below, the selection of filtering coefficients K andattenuation factors may depend on the motion and luma table selected forthe current color (e.g., the color of the current input pixel).

A method 338 illustrating a further embodiment for temporal filteringusing color-dependent motion and luma tables is shown in FIG. 27. Aswill be appreciated, the various calculations and formulas that may beemployed by the method 338 may be similar to the embodiment shown inFIG. 23, but with a particular motion and luma table being selected foreach color, or similar to the embodiment shown in FIG. 25, but replacingthe use of the color dependent gain[c] with the selection of acolor-dependent motion and luma table.

Beginning at step 339, a current pixel x(t) located at spatial location(j,i) of a current frame of image data is received by the temporalfiltering system 336 (FIG. 26). At step 340, a motion delta value d(t)is determined for the current pixel x(t) based at least partially uponone or more collocated reference pixels (e.g., r(t−1)) from a previousframe of the image data (e.g., the image frame immediately preceding thecurrent frame). Step 340 may be similar to the step 317 of FIG. 23, andmay utilize the operation shown in Equation 1 above.

Next, at step 341, a motion table lookup index may be determined usingthe motion delta value d(t) and a motion history input value h(t−1)corresponding to the spatial location (j,i) from the previous frame(e.g., corresponding to the collocated reference pixel r(t−1)).Thereafter, at step 342, a first filter coefficient K may be selectedfrom one of the available motion tables (e.g., 304 a, 304 b, 304 c)based upon the color of the current input pixel. For instance, one theappropriate motion table is identified, the first filter coefficient Kmay be selected using the motion table lookup index determined in step341.

After selecting the first filter coefficient K, a luma tablecorresponding to the current color is selected and an attenuation factoris selected from the selected luma table based upon the value of thecurrent pixel x(t), as shown at step 343. Thereafter, at step 344, asecond filter coefficient K′ is determined based upon the attenuationfactor (from step 343) and the first filter coefficient K (step 342).Next, at step 345, a temporally filtered output value y(t) correspondingto the current input pixel x(t) is determined based upon the secondfilter coefficient K′ (from step 344), the value of the collocatedreference pixel r(t−1), and the value of the input pixel x(t). While thetechnique shown in FIG. 27 may be more costly to implement (e.g., due tothe memory needed for storing additional motion and luma tables), itmay, in some instances, offer further improvements with regard toghosting artifacts and consistency across different colors afterauto-white balance gains.

In accordance with further embodiments, the temporal filtering processprovided by the temporal filter 298 may utilize a combination ofcolor-dependent gains and color-specific motion and/or luma tables forapplying temporal filtering to the input pixels. For instance, in onesuch embodiment, a single motion table may be provided for all colorcomponents, and the motion table lookup index for selecting the firstfiltering coefficient (K) from the motion table may be determined basedupon a color dependent gain (e.g., as shown in FIG. 25, steps 331-332),while the luma table lookup index may not have a color dependent gainapplied thereto, but may be used to select the brightness attenuationfactor from one of multiple luma tables depending upon the color of thecurrent input pixel (e.g., as shown in FIG. 27, step 343).Alternatively, in another embodiment, multiple motion tables may beprovided and a motion table lookup index (without a color dependent gainapplied) may be used to select the first filtering coefficient (K) froma motion table corresponding to the color of the current input pixel(e.g., as shown in FIG. 27, step 342), while a single luma table may beprovided for all color components, and wherein the luma table lookupindex for selecting the brightness attenuation factor may be determinedbased upon a color dependent gain (e.g., as shown in FIG. 25, steps333-334). Further, in one embodiment where a Bayer color filter array isutilized, one motion table and/or luma table may be provided for each ofthe red (R) and blue (B) color components, while a common motion tableand/or luma table may be provided for both green color components (Grand Gb).

The output of the temporal filter 298 may subsequently be sent to thebinning compensation filter (BCF) 300, which may be configured toprocess the image pixels to compensate for non-linear placement (e.g.,uneven spatial distribution) of the color samples due to binning by theimage sensor(s) 90 a or 90 b, such that subsequent image processingoperations in the ISP pipe logic 82 (e.g., demosaicing, etc.) thatdepend on linear placement of the color samples can operate correctly.For example, referring now to FIG. 28, a full resolution sample 346 ofBayer image data is depicted. This may represent a full resolutionsample raw image data captured by the image sensor 90 a (or 90 b)coupled to the ISP front-end processing logic 80.

As will be appreciated, under certain image capture conditions, it maybe not be practical to send the full resolution image data captured bythe image sensor 90 a to the ISP circuitry 32 for processing. Forinstance, when capturing video data, in order to preserve the appearanceof a fluid moving image from the perspective of the human eye, a framerate of at least approximately 30 frames per second may be desired.However, if the amount of pixel data contained in each frame of a fullresolution sample exceeds the processing capabilities of the ISPcircuitry 32 when sampled at 30 frames per second, binning compensationfiltering may be applied in conjunction with binning by the image sensor90 a to reduce the resolution of the image signal while also improvingsignal-to-noise ratio. For instance, as discussed above, various binningtechniques, such as 2×2 binning, may be applied to produce a “binned”raw image pixel by averaging the values of surrounding pixels in theactive region 280 of the raw frame 278.

Referring to FIG. 29, an embodiment of the image sensor 90 a that may beconfigured to bin the full resolution image data 346 of FIG. 28 toproduce corresponding binned raw image data 358 shown in FIG. 30 isillustrated in accordance with one embodiment. As shown, the imagesensor 90 a may capture the full resolution raw image data 346. Binninglogic 357 may be configured to apply binning to the full resolution rawimage data 346 to produce the binned raw image data 358, which may beprovided to the ISP front-end processing logic 80 using the sensorinterface 94 a which, as discussed above, may be an SMIA interface orany other suitable parallel or serial camera interfaces.

As illustrated in FIG. 30, the binning logic 357 may apply 2×2 binningto the full resolution raw image data 346. For example, with regard tothe binned image data 358, the pixels 350, 352, 354, and 356 may form aBayer pattern and may be determined by averaging the values of thepixels from the full resolution raw image data 346. For instance,referring to both FIGS. 28 and 30, the binned Gr pixel 350 may bedetermined as the average or mean of the full resolution Gr pixels 350a-350 d. Similarly, the binned R pixel 352 may be determined as theaverage of the full resolution R pixels 352 a-352 d, the binned B pixel354 may be determined as the average of the full resolution B pixels 354a-354 d, and the binned Gb pixel 356 may be determined as the average ofthe full resolution Gb pixels 356 a-356 d. Thus, in the presentembodiment, 2×2 binning may provide a set of four full resolution pixelsincluding an upper left (e.g., 350 a), upper right (e.g., 350 b), lowerleft (e.g., 350 c), and lower right (e.g., 350 d) pixel that areaveraged to derive a binned pixel located at the center of a squareformed by the set of four full resolution pixels. Accordingly, thebinned Bayer block 348 shown in FIG. 30 contains four “superpixels” thatrepresent the 16 pixels contained in the Bayer blocks 348 a-348 d ofFIG. 28.

In addition to reducing spatial resolution, binning also offers theadded advantage of reducing noise in the image signal. For instance,whenever an image sensor (e.g., 90 a) is exposed to a light signal,there may be a certain amount of noise, such as photon noise, associatedwith the image. This noise may be random or systematic and it also maycome from multiple sources. Thus, the amount of information contained inan image captured by the image sensor may be expressed in terms of asignal-to-noise ratio. For example, every time an image is captured byan image sensor 90 a and transferred to a processing circuit, such asthe ISP circuitry 32, there may be some degree of noise in the pixelsvalues because the process of reading and transferring the image datainherently introduces “read noise” into the image signal. This “readnoise” may be random and is generally unavoidable. By using the averageof four pixels, noise, (e.g., photon noise) may generally be reducedirrespective of the source of the noise.

Thus, when considering the full resolution image data 346 of FIG. 28,each Bayer pattern (2×2 block) 348 a-348 d contains 4 pixels, each ofwhich contains a signal and noise component. If each pixel in, forexample, the Bayer block 348 a, is read separately, then four signalcomponents and four noise components are present. However, by applyingbinning, as shown in FIGS. 28 and 30, such that four pixels (e.g., 350a, 350 b, 350 c, 350 d) may be represented by a single pixel (e.g., 350)in the binned image data, the same area occupied by the four pixels inthe full resolution image data 346 may be read as a single pixel withonly one instance of a noise component, thus improving signal-to-noiseratio.

Further, while the present embodiment depicts the binning logic 357 ofFIG. 29 as being configured to apply a 2×2 binning process, it should beappreciated that the binning logic 357 may be configured to apply anysuitable type of binning process, such as 3×3 binning, vertical binning,horizontal binning, and so forth. In some embodiments, the image sensor90 a may be configured to select between different binning modes duringthe image capture process. Additionally, in further embodiments, theimage sensor 90 a may also be configured to apply a technique that maybe referred to as “skipping,” wherein instead of average pixel samples,the logic 357 selects only certain pixels from the full resolution data346 (e.g., every other pixel, every 3 pixels, etc.) to output to the ISPfront-end 80 for processing. Further, while only the image sensor 90 ais shown in FIG. 29, it should be appreciated that the image sensor 90 bmay be implemented in a similar manner.

As also depicted in FIG. 30, one effect of the binning process is thatthe spatial sampling of the binned pixels may not be equally spaced.This spatial distortion may, in some systems, result in aliasing (e.g.,jagged edges), which is generally not desirable. Further, becausecertain image processing steps in the ISP pipe logic 82 may depend uponon the linear placement of the color samples in order to operatecorrectly, the binning compensation filter (BCF) 300 may be applied toperform re-sampling and re-positioning of the binned pixels such thatthe binned pixels are spatially evenly distributed. That is, the BCF 300essentially compensates for the uneven spatial distribution (e.g., shownin FIG. 30) by re-sampling the position of the samples (e.g., pixels).For instance, FIG. 31 illustrates a re-sampled portion of binned imagedata 360 after being processed by the BCF 300, wherein the Bayer block361 containing the evenly distributed re-sampled pixels 362, 363, 364,and 365 correspond to the binned pixels 350, 352, 354, and 356,respectively, of the binned image data 358 from FIG. 30. Additionally,in an embodiment that utilizes skipping (e.g., instead of binning), asmentioned above, the spatial distortion shown in FIG. 30 may not bepresent. In this case, the BCF 300 may function as a low pass filter toreduce artifacts (e.g., aliasing) that may result when skipping isemployed by the image sensor 90 a.

FIG. 32 shows a block diagram of the binning compensation filter 300 inaccordance with one embodiment. The BCF 300 may include binningcompensation logic 366 that may process binned pixels 358 to applyhorizontal and vertical scaling using horizontal scaling logic 368 andvertical scaling logic 370, respectively, to re-sample and re-positionthe binned pixels 358 so that they are arranged in a spatially evendistribution, as shown in FIG. 31. In one embodiment, the scalingoperation(s) performed by the BCF 300 may be performed using horizontaland vertical multi-tap polyphase filtering. For instance, the filteringprocess may include selecting the appropriate pixels from the inputsource image data (e.g., the binned image data 358 provided by the imagesensor 90 a), multiplying each of the selected pixels by a filteringcoefficient, and summing up the resulting values to form an output pixelat a desired destination.

The selection of the pixels used in the scaling operations, which mayinclude a center pixel and surrounding neighbor pixels of the samecolor, may be determined using separate differential analyzers 372, onefor vertical scaling and one for horizontal scaling. In the depictedembodiment, the differential analyzers 372 may be digital differentialanalyzers (DDAs) and may be configured to control the current outputpixel position during the scaling operations in the vertical andhorizontal directions. In the present embodiment, a first DDA (referredto as 372 a) is used for all color components during horizontal scaling,and a second DDA (referred to as 372 b) is used for all color componentsduring vertical scaling. By way of example only, the DDA 372 may beprovided as a 32-bit data register that contains a 2's-complementfixed-point number having 16 bits in the integer portion and 16 bits inthe fraction. The 16-bit integer portion may be used to determine thecurrent position for an output pixel. The fractional portion of the DDA372 may be used to determine a current index or phase, which may bebased the between-pixel fractional position of the current DDA position(e.g., corresponding to the spatial location of the output pixel). Theindex or phase may be used to select an appropriate set of coefficientsfrom a set of filter coefficient tables 374. Additionally, the filteringmay be done per color component using same colored pixels. Thus, thefiltering coefficients may be selected based not only on the phase ofthe current DDA position, but also the color of the current pixel. Inone embodiment, 8 phases may be present between each input pixel and,thus, the vertical and horizontal scaling components may utilize 8-deepcoefficient tables, such that the high-order 3 bits of the 16-bitfraction portion are used to express the current phase or index. Thus,as used herein, the term “raw image” data or the like shall beunderstood to refer to multi-color image data that is acquired by asingle sensor with a color filter array pattern (e.g., Bayer) overlayingit, those providing multiple color components in one plane. In anotherembodiment, separate DDAs may be used for each color component. Forinstance, in such embodiments, the BCF 300 may extract the R, B, Gr, andGb components from the raw image data and process each component as aseparate plane.

In operation, horizontal and vertical scaling may include initializingthe DDA 372 and performing the multi-tap polyphase filtering using theinteger and fractional portions of the DDA 372. While performedseparately and with separate DDAs, the horizontal and vertical scalingoperations are carried out in a similar manner. A step value or stepsize (DDAStepX for horizontal scaling and DDAStepY for vertical scaling)determines how much the DDA value (currDDA) is incremented after eachoutput pixel is determined, and multi-tap polyphase filtering isrepeated using the next currDDA value. For instance, if the step valueis less than 1, then the image is up-scaled, and if the step value isgreater than 1, the image is downscaled. If the step value is equal to1, then no scaling occurs. Further, it should be noted that same ordifferent step sizes may be used for horizontal and vertical scaling.

Output pixels are generated by the BCF 300 in the same order as inputpixels (e.g., using the Bayer pattern). In the present embodiment, theinput pixels may be classified as being even or odd based on theirordering. For instance, referring to FIG. 33, a graphical depiction ofinput pixel locations (row 375) and corresponding output pixel locationsbased on various DDAStep values (rows 376-380) are illustrated. In thisexample, the depicted row represents a row of red (R) and green (Gr)pixels in the raw Bayer image data. For horizontal filtering purposes,the red pixel at position 0.0 in the row 375 may be considered an evenpixel, the green pixel at position 1.0 in the row 375 may be consideredan odd pixel, and so forth. For the output pixel locations, even and oddpixels may be determined based on the least significant bit in thefraction portion (lower 16 bits) of the DDA 372. For instance, assuminga DDAStep of 1.25, as shown in row 377, the least significant bitcorresponds to the bit 14 of the DDA, as this bit gives a resolution of0.25. Thus, the red output pixel at the DDA position (currDDA) 0.0 maybe considered an even pixel (the least significant bit, bit 14, is 0),the green output pixel at currDDA 1.0 (bit 14 is 1), and so forth.Further, while FIG. 33 is discussed with respect to filtering in thehorizontal direction (using DDAStepX), it should be understood that thedetermination of even and odd input and output pixels may be applied inthe same manner with respect to vertical filtering (using DDAStepY). Inother embodiments, the DDAs 372 may also be used to track locations ofthe input pixels (e.g., rather than track the desired output pixellocations). Further, it should be appreciated that DDAStepX and DDAStepYmay be set to the same or different values. Further, assuming a Bayerpattern is used, it should be noted that the starting pixel used by theBCF 300 could be any one of a Gr, Gb, R, or B pixel depending, forinstance, on which pixel is located at a corner within the active region280.

With this in mind, the even/odd input pixels are used to generate theeven/odd output pixels, respectively. Given an output pixel locationalternating between even and odd position, a center source input pixellocation (referred to herein as “currPixel”) for filtering purposes isdetermined by the rounding the DDA to the closest even or odd inputpixel location for even or odd output pixel locations (based onDDAStepX), respectively. In an embodiment where the DDA 372 a isconfigured to use 16 bits to represent an integer and 16 bits torepresent a fraction, currPixel may be determined for even and oddcurrDDA positions using Equations 6a and 6b below:

Even output pixel locations may be determined based on bits [31:16] of:(currDDA+1.0) & 0xFFFE.0000  (6a)

Odd output pixel locations may be determined based on bits [31:16] of:(currDDA)|0x0001.0000  (6b)

Essentially, the above equations present a rounding operation, wherebythe even and odd output pixel positions, as determined by currDDA, arerounded to the nearest even and odd input pixel positions, respectively,for the selection of currPixel.

Additionally, a current index or phase (currindex) may also bedetermined at each currDDA position. As discussed above, the index orphase values represent the fractional between-pixel position of theoutput pixel position relative to the input pixel positions. Forinstance, in one embodiment, 8 phases may be defined between each inputpixel position. For instance, referring again to FIG. 33, 8 index values0-7 are provided between the first red input pixel at position 0.0 andthe next red input pixel at position 2.0. Similarly, 8 index values 0-7are provided between the first green input pixel at position 1.0 and thenext green input pixel at position 3.0. In one embodiment, the currIndexvalues may be determined in accordance with Equations 7a and 7b belowfor even and odd output pixel locations, respectively:

Even output pixel locations may be determined based on bits [16:14] of:(currDDA+0.125)  (7a)

Odd output pixel locations may be determined based on bits [16:14] of:(currDDA+1.125)  (7b)

For the odd positions, the additional 1 pixel shift is equivalent toadding an offset of four to the coefficient index for odd output pixellocations to account for the index offset between different colorcomponents with respect to the DDA 372.

Once currPixel and currIndex have been determined at a particularcurrDDA location, the filtering process may select one or moreneighboring same-colored pixels based on currPixel (the selected centerinput pixel). By way of example, in an embodiment where the horizontalscaling logic 368 includes a 5-tap polyphase filter and the verticalscaling logic 370 includes a 3-tap polyphase filter, two same-coloredpixels on each side of currPixel in the horizontal direction may beselected for horizontal filtering (e.g., −2, −1, 0, +1, +2), and onesame-colored pixel on each side of currPixel in the vertical directionmay be selected for vertical filtering (e.g., −1, 0, +1). Further,currIndex may be used as a selection index to select the appropriatefiltering coefficients from the filter coefficients table 374 to applyto the selected pixels. For instance, using the 5-tap horizontal/3-tapvertical filtering embodiment, five 8-deep tables may be provided forhorizontal filtering, and three 8-deep tables may be provided forvertical filtering. Though illustrated as part of the BCF 300, it shouldbe appreciated that the filter coefficient tables 374 may, in certainembodiments, be stored in a memory that is physically separate from theBCF 300, such as the memory 108.

Before discussing the horizontal and vertical scaling operations infurther detail, Table 4 below shows examples of how currPixel andcurrIndex values, as determined based on various DDA positions usingdifferent DDAStep values (e.g., could apply to DDAStepX or DDAStepY).

TABLE 4 Binning Compensation Filter - DDA Examples of currPixel andcurrIndex calculation Out- put Pixel (Even or DDAStep 1.25 DDAStep 1.5DDAStep 1.75 DDAStep 2.0 Odd) currDDA currIndex currPixel currDDAcurrIndex currPixel currDDA currIndex currPixel currDDA currIndexcurrPixel 0 0.0 0 0 0.0 0 0 0.0 0 0 0.0 0 0 1 1.25 1 1 1.5 2 1 1.75 3 12 4 3 0 2.5 2 2 3 4 4 3.5 6 4 4 0 4 1 3.75 3 3 4.5 6 5 5.25 1 5 6 4 7 05 4 6 6 0 6 7 4 8 8 0 8 1 6.25 5 7 7.5 2 7 8.75 7 9 10 4 11 0 7.5 6 8 94 10 10.5 2 10 12 0 12 1 8.75 7 9 10.5 6 11 12.25 5 13 14 4 15 0 10 0 1012 0 12 14 0 14 16 0 16 1 11.25 1 11 13.5 2 13 15.75 3 15 18 4 19 0 12.52 12 15 4 16 17.5 6 18 20 0 20 1 13.75 3 13 16.5 6 17 19.25 1 19 22 4 230 15 4 16 18 0 18 21 4 22 24 0 24 1 16.25 5 17 19.5 2 19 22.75 7 23 26 427 0 17.5 6 18 21 4 22 24.5 2 24 28 0 28 1 18.75 7 19 22.5 6 23 26.25 527 30 4 31 0 20 0 20 24 0 24 28 0 28 32 0 32

To provide an example, let us assume that a DDA step size (DDAStep) of1.5 is selected (row 378 of FIG. 33), with the current DDA position(currDDA) beginning at 0, indicating an even output pixel position. Todetermine currPixel, Equation 6a may be applied, as shown below:

currDDA = 0.0(even) ${{({AND})\begin{matrix}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.\; 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} \left( {{currDDA} + 1.0} \right)} \\{1111\mspace{14mu} 1111\mspace{14mu} 1111\mspace{14mu} 1110.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{25mu} \left( {0{xFFFE}{.0000}} \right)} \\{{\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}{.0000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currPixel}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {31:16} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = 0}};$

Thus, at the currDDA position 0.0 (row 378), the source input centerpixel for filtering corresponds to the red input pixel at position 0.0of row 375.

To determine currIndex at the even currDDA 0.0, Equation 7a may beapplied, as shown below:

${{currDDA} = {{{0.0({even})}\mspace{40mu} + \; \begin{matrix}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.\; 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{70mu} ({currDDA})} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{110mu} (0.125)}\;} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 000\underset{\_}{0.00}10\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currIndex}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {16:14} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = {\lbrack 000\rbrack = 0}}}};$

Thus, at the currDDA position 0.0 (row 378), a currIndex value of 0 maybe used to select filtering coefficients from the filter coefficientstable 374.

Accordingly, filtering (which may be vertical or horizontal depending onwhether DDAStep is in the X (horizontal) or Y (vertical) direction) mayapplied based on the determined currPixel and currIndex values atcurrDDA 0.0, and the DDA 372 is incremented by DDAStep (1.5), and thenext currPixel and currIndex values are determined. For instance, at thenext currDDA position 1.5 (an odd position), currPixel may be determinedusing Equation 6b as follows:

 currDDA = 0.0(odd) ${{({OR})\mspace{14mu} \begin{matrix}{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.\mspace{11mu} 1000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} ({currDDA})}\mspace{50mu}} \\{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{25mu} \left( {0x\; 0001.0000} \right)} \\{{\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001}{.0000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currPixel}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {31:16} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = 1}};$

Thus, at the currDDA position 1.5 (row 378), the source input centerpixel for filtering corresponds to the green input pixel at position 1.0of row 375.

Further, currIndex at the odd currDDA 1.5 may be determined usingEquation 7b, as shown below:

${{currDDA} = {{{1.5({odd})}\mspace{40mu} + \; \begin{matrix}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.\mspace{11mu} 1000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{70mu} ({currDDA})} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0001.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{110mu} (1.125)}\;} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 001\underset{\_}{0.\mspace{11mu} 10}10\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currIndex}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {16:14} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = {\lbrack 010\rbrack = 2}}}};$

Thus, at the currDDA position 1.5 (row 378), a currIndex value of 2 maybe used to select the appropriate filtering coefficients from the filtercoefficients table 374. Filtering (which may be vertical or horizontaldepending on whether DDAStep is in the X (horizontal) or Y (vertical)direction) may thus be applied using these currPixel and currIndexvalues.

Next, the DDA 372 is incremented again by DDAStep (1.5), resulting in acurrDDA value of 3.0. The currPixel corresponding to currDDA 3.0 may bedetermined using Equation 6a, as shown below:

 currDDA = 3.0(even) ${{({AND})\begin{matrix}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0100.\; 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} \left( {{currDDA} + 1.0} \right)} \\{1111\mspace{14mu} 1111\mspace{14mu} 1111\mspace{14mu} 1110.0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{25mu} \left( {0{xFFFE}{.0000}} \right)} \\{{\underset{\_}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0100}{.0000}\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currPixel}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {31:16} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = 4}};$

Thus, at the currDDA position 3.0 (row 378), the source input centerpixel for filtering corresponds to the red input pixel at position 4.0of row 375.

Next, currIndex at the even currDDA 3.0 may be determined using Equation7a, as shown below:

$\; {{{currDDA} = {{{3.0({even})}\mspace{40mu} + \; \begin{matrix}{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0011.\mspace{11mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{70mu} ({currDDA})} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000.0010\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{110mu} (0.125)}\;} \\{{0000\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 001\underset{\_}{1.\mspace{11mu} 00}10\mspace{14mu} 0000\mspace{14mu} 0000\mspace{14mu} 0000}\mspace{185mu}}\end{matrix}}\mspace{40mu} = {{{currIndex}\mspace{14mu} \left( {{determined}\mspace{14mu} {as}\mspace{14mu} {{bits}\left\lbrack {16:14} \right\rbrack}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {result}} \right)} = {\lbrack 100\rbrack = 4}}}};}$

Thus, at the currDDA position 3.0 (row 378), a currIndex value of 4 maybe used to select the appropriate filtering coefficients from the filtercoefficients table 374. As will be appreciated, the DDA 372 may continueto be incremented by DDAStep for each output pixel, and filtering (whichmay be vertical or horizontal depending on whether DDAStep is in the X(horizontal) or Y (vertical) direction) may be applied using thecurrPixel and currIndex determined for each currDDA value.

As discussed above, currIndex may be used as a selection index to selectthe appropriate filtering coefficients from the filter coefficientstable 374 to apply to the selected pixels. The filtering process mayinclude obtaining the source pixel values around the center pixel(currPixel), multiplying each of the selected pixels by the appropriatefiltering coefficients selected from the filter coefficients table 374based on currIndex, and summing the results to obtain a value of theoutput pixel at the location corresponding to currDDA. Further, becausethe present embodiment utilizes 8 phases between same colored pixels,using the 5-tap horizontal/3-tap vertical filtering embodiment, five8-deep tables may be provided for horizontal filtering, and three 8-deeptables may be provided for vertical filtering. In one embodiment, eachof the coefficient table entries may include a 16-bit 2's complementfixed point number with 3 integer bits and 13 fraction bits.

Further, assuming a Bayer image pattern, in one embodiment, the verticalscaling component may include four separate 3-tap polyphase filters, onefor each color component: Gr, R, B, and Gb. Each of the 3-tap filtersmay use the DDA 372 to control the stepping of the current center pixeland the index for the coefficients, as described above. Similarly, thehorizontal scaling components may include four separate 5-tap polyphasefilters, one for each color component: Gr, R, B, and Gb. Each of the5-tap filters may use the DDA 372 to control the stepping (e.g., viaDDAStep) of the current center pixel and the index for the coefficients.It should be understood however, that fewer or more taps could beutilized by the horizontal and vertical scalars in other embodiments.

For boundary cases, the pixels used in the horizontal and verticalfiltering process may depend upon the relationship of the current DDAposition (currDDA) relative to a frame border (e.g., border defined bythe active region 280 in FIG. 19). For instance, in horizontalfiltering, if the currDDA position, when compared to the position of thecenter input pixel (SrcX) and the width (SrcWidth) of the frame (e.g.,width 290 of the active region 280 of FIG. 19) indicates that the DDA372 is close to the border such that there are not enough pixels toperform the 5-tap filtering, then the same-colored input border pixelsmay be repeated. For instance, if the selected center input pixel is atthe left edge of the frame, then the center pixel may be replicatedtwice for horizontal filtering. If the center input pixel is near theleft edge of the frame such that only one pixel is available between thecenter input pixel and the left edge, then, for horizontal filteringpurposes, the one available pixel is replicated in order to provide twopixel values to the left of the center input pixel. Further, thehorizontal scaling logic 368 may be configured such that the number ofinput pixels (including original and replicated pixels) cannot exceedthe input width. This may be expressed as follows:

StartX=(((DDAInitX+0x0001.0000) & 0xFFFE.0000)>>16)

EndX=(((DDAInitX+DDAStepX*(BCFOutWidth−1))|0x0001.0000)>>16)

EndX−StartX<=SrcWidth−1

wherein, DDAInitX represents the initial position of the DDA 372,DDAStepX represents the DDA step value in the horizontal direction, andBCFOutWidth represents the width of the frame output by the BCF 300.

For vertical filtering, if the currDDA position, when compared to theposition of the center input pixel (SrcY) and the width (SrcHeight) ofthe frame (e.g., width 290 of the active region 280 of FIG. 19)indicates that the DDA 372 is close to the border such that there arenot enough pixels to perform the 3-tap filtering, then the input borderpixels may be repeated. Further, the vertical scaling logic 370 may beconfigured such that the number of input pixels (including original andreplicated pixels) cannot exceed the input height. This may be expressedas follows:

StartY=(((DDAInitY+0x0001.0000) & 0xFFFE.0000)>>16)

EndY=(((DDAInitY+DDAStepY*(BCFOutHeight−1))|0x0001.0000)>>16)

EndY−StartY<=SrcHeight−1

wherein, DDAInitY represents the initial position of the DDA 372,DDAStepY represents the DDA step value in the vertical direction, andBCFOutHeight represents the width of the frame output by the BCF 300.

Referring now to FIG. 34, a flow chart depicting a method 382 forapplying binning compensation filtering to image data received by thefront-end pixel processing unit 130 in accordance with an embodiment. Itwill be appreciated that the method 382 illustrated in FIG. 34 may applyto both vertical and horizontal scaling. Beginning at step 383 the DDA372 is initialized and a DDA step value (which may correspond toDDAStepX for horizontal scaling and DDAStepY for vertical scaling) isdetermined. Next, at step 384, a current DDA position (currDDA), basedon DDAStep, is determined. As discussed above, currDDA may correspond toan output pixel location. Using currDDA, the method 382 may determine acenter pixel (currPixel) from the input pixel data that may be used forbinning compensation filtering to determine a corresponding output valueat currDDA, as indicated at step 385. Subsequently, at step 386, anindex corresponding to currDDA (currIndex) may be determined based onthe fractional between-pixel position of currDDA relative to the inputpixels (e.g., row 375 of FIG. 33). By way of example, in an embodimentwhere the DDA includes 16 integer bits and 16 fraction bits, currPixelmay be determined in accordance with Equations 6a and 6b, and currIndexmay be determined in accordance with Equations 7a and 7b, as shownabove. While the 16 bit integer/16 bit fraction configuration isdescribed herein as one example, it should be appreciated that otherconfigurations of the DDA 372 may be utilized in accordance with thepresent technique. By way of example, other embodiments of the DDA 372may be configured to include a 12 bit integer portion and 20 bitfraction portion, a 14 bit integer portion and 18 bit fraction portion,and so forth.

Once currPixel and currIndex are determined, same-colored source pixelsaround currPixel may be selected for multi-tap filtering, as indicatedby step 387. For instance, as discussed above, one embodiment mayutilize 5-tap polyphase filtering in the horizontal direction (e.g.,selecting 2 same-colored pixels on each side of currPixel) and mayutilize 3-tap polyphase filtering in the vertical direction (e.g.,selecting 1 same-colored pixel on each side of currPixel). Next, at step388, once the source pixels are selected, filtering coefficients may beselected from the filter coefficients table 374 of the BCF 300 basedupon currIndex.

Thereafter, at step 389, filtering may be applied to the source pixelsto determine the value of an output pixel corresponding to the positionrepresented by currDDA. For instance, in one embodiment, the sourcepixels may be multiplied by their respective filtering coefficients, andthe results may be summed to obtain the output pixel value. Thedirection in which filtering is applied at step 389 may be vertical orhorizontal depending on whether DDAStep is in the X (horizontal) or Y(vertical) direction. Finally, at step 263, the DDA 372 is incrementedby DDAStep at step 390, and the method 382 returns to step 384, wherebythe next output pixel value is determined using the binning compensationfiltering techniques discussed herein.

Referring to FIG. 35, the step 385 for determining currPixel from themethod 382 is illustrated in more detail in accordance with oneembodiment. For instance, step 385 may include the sub-step 392 ofdetermining whether the output pixel location corresponding to currDDA(from step 384) is even or odd. As discussed above, an even or oddoutput pixel may be determined based on the least significant bit ofcurrDDA based on DDAStep. For instance, given a DDAStep of 1.25, acurrDDA value of 1.25 may be determined as odd, since the leastsignificant bit (corresponding to bit 14 of the fractional portion ofthe DDA 372) has a value of 1. For a currDDA value of 2.5, bit 14 is 0,thus indicating an even output pixel location.

At decision logic 393, a determination is made as to whether the outputpixel location corresponding to currDDA is even or odd. If the outputpixel is even, decision logic 393 continues to sub-step 394, whereincurrPixel is determined by incrementing the currDDA value by 1 androunding the result to the nearest even input pixel location, asrepresented by Equation 6a above. If the output pixel is odd, thendecision logic 393 continues to sub-step 395, wherein currPixel isdetermined by rounding the currDDA value to the nearest odd input pixellocation, as represented by Equation 6b above. The currPixel value maythen be applied to step 387 of the method 382 to select source pixelsfor filtering, as discussed above.

Referring also to FIG. 36, the step 386 for determining currIndex fromthe method 382 is illustrated in more detail in accordance with oneembodiment. For instance, step 386 may include the sub-step 396 ofdetermining whether the output pixel location corresponding to currDDA(from step 384) is even or odd. This determination may be performed in asimilar manner as step 392 of FIG. 35. At decision logic 397, adetermination is made as to whether the output pixel locationcorresponding to currDDA is even or odd. If the output pixel is even,decision logic 397 continues to sub-step 398, wherein currIndex isdetermined by incrementing the currDDA value by one index stepdetermining currIndex based on the lowest order integer bit and the twohighest order fraction bits of the DDA 372. For instance, in anembodiment wherein 8 phases are provided between each same-coloredpixel, and wherein the DDA includes 16 integer bits and 16 fractionbits, one index step may correspond to 0.125, and currIndex may bedetermined based on bits [16:14] of the currDDA value incremented by0.125 (e.g., Equation 7a). If the output pixel is odd, decision logic397 continues to sub-step 399, wherein currIndex is determined byincrementing the currDDA value by one index step and one pixel shift,and determining currIndex based on the lowest order integer bit and thetwo highest order fraction bits of the DDA 372. Thus, in an embodimentwherein 8 phases are provided between each same-colored pixel, andwherein the DDA includes 16 integer bits and 16 fraction bits, one indexstep may correspond to 0.125, one pixel shift may correspond to 1.0 (ashift of 8 index steps to the next same colored pixel), and currIndexmay be determined based on bits [16:14] of the currDDA value incrementedby 1.125 (e.g., Equation 7b).

While the presently illustrated embodiment provides the BCF 300 as acomponent of the front-end pixel processing unit 130, other embodimentsmay incorporate the BCF 300 into a raw image data processing pipeline ofthe ISP pipe 82 which, as discussed further below, may include defectivepixel detection/correction logic, gain/offset/compensation blocks, noisereduction logic, lens shading correction logic, and demosaicing logic.Further, in embodiments where the aforementioned defective pixeldetection/correction logic, gain/offset/compensation blocks, noisereduction logic, lens shading correction logic do not rely upon thelinear placement of the pixels, the BCF 300 may be incorporated with thedemosaicing logic to perform binning compensation filtering andreposition the pixels prior to demoasicing, as demosaicing generallydoes rely upon the even spatial positioning of the pixels. For instance,in one embodiment, the BCF 300 may be incorporated anywhere between thesensor input and the demosaicing logic, with temporal filtering and/ordefective pixel detection/correction being applied to the raw image dataprior to binning compensation.

As discussed above the output of the BCF 300, which may be the outputFEProcOut (109) having spatially evenly distributed image data (e.g.,sample 360 of FIG. 31), may be forwarded to the ISP pipe processinglogic 82 for additional processing. However, before shifting the focusof this discussion to the ISP pipe processing logic 82, a more detaileddescription of various functionalities that may be provided by thestatistics processing units (e.g., 122 and 124) that may be implementedin the ISP front-end logic 80 will first be provided.

Referring back to the general description of the statistics processingunits 120 and 122, these units may be configured to collect variousstatistics about the image sensors that capture and provide the rawimage signals (Sif0 and Sif1), such as statistics relating toauto-exposure, auto-white balance, auto-focus, flicker detection, blacklevel compensation, and lens shading correction, and so forth. In doingso, the statistics processing units 120 and 122 may first apply one ormore image processing operations to their respective input signals, Sif0(from Sensor0) and Sif1 (from Sensor1).

For example, referring to FIG. 37, a more detailed block diagram view ofthe statistics processing unit 120 associated with Sensor0 (90 a) isillustrated in accordance with one embodiment. As shown, the statisticsprocessing unit 120 may include the following functional blocks:defective pixel detection and correction logic 460, black levelcompensation (BLC) logic 462, lens shading correction logic 464, inverseBLC logic 466, and statistics collection logic 468. Each of thesefunctional blocks will be discussed below. Further, it should beunderstood that the statistics processing unit 122 associated withSensor1 (90 b) may be implemented in a similar manner.

Initially, the output of selection logic 124 (e.g., Sif0 or SifIn0) isreceived by the front-end defective pixel correction logic 460. As willbe appreciated, “defective pixels” may be understood to refer to imagingpixels within the image sensor(s) 90 that fail to sense light levelsaccurately. Defective pixels may attributable to a number of factors,and may include “hot” (or leaky) pixels, “stuck” pixels, and “deadpixels.” A “hot” pixel generally appears as being brighter than anon-defective pixel given the same amount of light at the same spatiallocation. Hot pixels may result due to reset failures and/or highleakage. For example, a hot pixel may exhibit a higher than normalcharge leakage relative to non-defective pixels, and thus may appearbrighter than non-defective pixels. Additionally, “dead” and “stuck”pixels may be the result of impurities, such as dust or other tracematerials, contaminating the image sensor during the fabrication and/orassembly process, which may cause certain defective pixels to be darkeror brighter than a non-defective pixel, or may cause a defective pixelto be fixed at a particular value regardless of the amount of light towhich it is actually exposed. Additionally, dead and stuck pixels mayalso result from circuit failures that occur during operation of theimage sensor. By way of example, a stuck pixel may appear as alwaysbeing on (e.g., fully charged) and thus appears brighter, whereas a deadpixel appears as always being off.

The defective pixel detection and correction (DPDC) logic 460 in the ISPfront-end logic 80 may correct (e.g., replace defective pixel values)defective pixels before they are considered in statistics collection(e.g., 468). In one embodiment, defective pixel correction is performedindependently for each color component (e.g., R, B, Gr, and Gb for aBayer pattern). Generally, the front-end DPDC logic 460 may provide fordynamic defect correction, wherein the locations of defective pixels aredetermined automatically based upon directional gradients computed usingneighboring pixels of the same color. As will be understand, the defectsmay be “dynamic” in the sense that the characterization of a pixel asbeing defective at a given time may depend on the image data in theneighboring pixels. By way of example, a stuck pixel that is always onmaximum brightness may not be regarded as a defective pixel if thelocation of the stuck pixel is in an area of the current image that isdominate by brighter or white colors. Conversely, if the stuck pixel isin a region of the current image that is dominated by black or darkercolors, then the stuck pixel may be identified as a defective pixelduring processing by the DPDC logic 460 and corrected accordingly.

The DPDC logic 460 may utilize one or more horizontal neighboring pixelsof the same color on each side of a current pixel to determine if thecurrent pixel is defective using pixel-to-pixel directional gradients.If a current pixel is identified as being defective, the value of thedefective pixel may be replaced with the value of a horizontalneighboring pixel. For instance, in one embodiment, five horizontalneighboring pixels of the same color that are inside the raw frame 278(FIG. 19) boundary are used, wherein the five horizontal neighboringpixels include the current pixel and two neighboring pixels on eitherside. Thus, as illustrated in FIG. 38, for a given color component c andfor the current pixel P, horizontal neighbor pixels P0, P1, P2, and P3may be considered by the DPDC logic 460. It should be noted, however,that depending on the location of the current pixel P, pixels outsidethe raw frame 278 are not considered when calculating pixel-to-pixelgradients.

For instance, as shown in FIG. 38, in a “left edge” case 470, thecurrent pixel P is at the leftmost edge of the raw frame 278 and, thus,the neighboring pixels P0 and P1 outside of the raw frame 278 are notconsidered, leaving only the pixels P, P2, and P3 (N=3). In a “leftedge+1” case 472, the current pixel P is one unit pixel away from theleftmost edge of the raw frame 278 and, thus, the pixel P0 is notconsidered. This leaves only the pixels P1, P, P2, and P3 (N=4).Further, in a “centered” case 474, pixels P0 and P1 on the left side ofthe current pixel P and pixels P2 and P3 on the right side of thecurrent pixel P are within the raw frame 278 boundary and, therefore,all of the neighboring pixels P0, P1, P2, and P3 (N=5) are considered incalculating pixel-to-pixel gradients. Additionally, similar cases 476and 478 may be encountered as the rightmost edge of the raw frame 278 isapproached. For instance, given the “right edge−1” case 476, the currentpixel P is one unit pixel away the rightmost edge of the raw frame 278and, thus, the pixel P3 is not considered (N=4). Similarly, in the“right edge” case 478, the current pixel P is at the rightmost edge ofthe raw frame 278 and, thus, both of the neighboring pixels P2 and P3are not considered (N=3).

In the illustrated embodiment, for each neighboring pixel (k=0 to 3)within the picture boundary (e.g., raw frame 278), the pixel-to-pixelgradients may be calculated as follows:

G _(k)=abs(P−P _(k)), for 0≦k≦3 (only for k within the raw frame)  (8)

Once the pixel-to-pixel gradients have been determined, defective pixeldetection may be performed by the DPDC logic 460 as follows. First, itis assumed that a pixel is defective if a certain number of itsgradients G_(k) are at or below a particular threshold, denoted by thevariable dprTh. Thus, for each pixel, a count (C) of the number ofgradients for neighboring pixels inside the picture boundaries that areat or below the threshold dprTh is accumulated. By way of example, foreach neighbor pixel inside the raw frame 278, the accumulated count C ofthe gradients G_(k) that are at or below the threshold dprTh may becomputed as follows:

$\begin{matrix}{{C = {\sum\limits_{k}^{N}\left( {G_{k} \leq {dprTh}} \right)}},{{{for}\mspace{14mu} 0} \leq k \leq {3\left( {{only}\mspace{14mu} {for}\mspace{14mu} k\mspace{14mu} {within}\mspace{14mu} {the}\mspace{14mu} {raw}\mspace{14mu} {frame}} \right)}}} & (9)\end{matrix}$

As will be appreciated, depending on the color components, the thresholdvalue dprTh may vary. Next, if the accumulated count C is determined tobe less than or equal to a maximum count, denoted by the variabledprMaxC, then the pixel may be considered defective. This logic isexpressed below:

if (C≦dprMaxC), then the pixel is defective.  (10)

Defective pixels are replaced using a number of replacement conventions.For instance, in one embodiment, a defective pixel may be replaced withthe pixel to its immediate left, P1. At a boundary condition (e.g., P1is outside of the raw frame 278), a defective pixel may replaced withthe pixel to its immediate right, P2. Further, it should be understoodthat replacement values may be retained or propagated for successivedefective pixel detection operations. For instance, referring to the setof horizontal pixels shown in FIG. 38, if P0 or P1 were previouslyidentified by the DPDC logic 460 as being defective pixels, theircorresponding replacement values may be used for the defective pixeldetection and replacement of the current pixel P.

To summarize the above-discussed defective pixel detection andcorrection techniques, a flow chart depicting such a process is providedin FIG. 39 and referred to by reference number 480. As shown, process480 begins at step 482, at which a current pixel (P) is received and aset of neighbor pixels is identified. In accordance with the embodimentdescribed above, the neighbor pixels may include two horizontal pixelsof the same color component from opposite sides of the current pixel(e.g., P0, P1, P2, and P3). Next, at step 484, horizontal pixel-to-pixelgradients are calculated with respect to each neighboring pixel withinthe raw frame 278, as described in Equation 8 above. Thereafter, at step486, a count C of the number of gradients that are less than or equal toa particular threshold dprTh is determined. As shown at decision logic488, if C is less than or equal to dprMaxC, then the process 480continues to step 490, and the current pixel is identified as beingdefective. The defective pixel is then corrected at step 492 using areplacement value. Additionally, referring back to decision logic 488,if C is greater than dprMaxC, then the process continues to step 494,and the current pixel is identified as not being defective, and itsvalue is not changed.

It should be noted that the defective pixel detection/correctiontechniques applied during the ISP front-end statistics processing may beless robust than defective pixel detection/correction that is performedin the ISP pipe logic 82. For instance, as will be discussed in furtherdetail below, defective pixel detection/correction performed in the ISPpipe logic 82 may, in addition to dynamic defect correction, furtherprovide for fixed defect correction, wherein the locations of defectivepixels are known a priori and loaded in one or more defect tables.Further, dynamic defect correction may in the ISP pipe logic 82 may alsoconsider pixel gradients in both horizontal and vertical directions, andmay also provide for the detection/correction of speckling, as will bediscussed below.

Returning to FIG. 37, the output of the DPDC logic 460 is then passed tothe black level compensation (BLC) logic 462. The BLC logic 462 mayprovide for digital gain, offset, and clipping independently for eachcolor component “c” (e.g., R, B, Gr, and Gb for Bayer) on the pixelsused for statistics collection. For instance, as expressed by thefollowing operation, the input value for the current pixel is firstoffset by a signed value, and then multiplied by a gain.

Y=(X+O[c])×G[c],  (11)

wherein X represents the input pixel value for a given color component c(e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for thecurrent color component c, and G[c] represents a gain value for thecolor component c. In one embodiment, the gain G[c] may be a 16-bitunsigned number with 2 integer bits and 14 fraction bits (e.g., 2.14 infloating point representation), and the gain G[c] may be applied withrounding. By way of example only, the gain G[c] may have a range ofbetween 0 to 4× (e.g., 4 times the input pixel value).

Next, as shown by Equation 12 below, the computed value Y, which issigned, may then be then clipped to a minimum and maximum range:

Y=(Y<min[c])?min[c]:(Y>max[c])?max[c]:Y)  (12)

The variables min[c] and max[c] may represent signed 16-bit “clippingvalues for the minimum and maximum output values, respectively. In oneembodiment, the BLC logic 462 may also be configured to maintain a countof the number of pixels that were clipped above and below maximum andminimum, respectively, per color component.

Subsequently, the output of the BLC logic 462 is forwarded to the lensshading correction (LSC) logic 464. The LSC logic 464 may be configuredto apply an appropriate gain on a per-pixel basis to compensate fordrop-offs in intensity, which are generally roughly proportional to thedistance from the optical center of the lens 88 of the imaging device30. As can be appreciated, such drop-offs may be the result of thegeometric optics of the lens. By way of example, a lens having idealoptical properties may be modeled as the fourth power of the cosine ofthe incident angle, cos⁴(θ), referred to as the cos⁴ law. However,because lens manufacturing is not perfect, various irregularities in thelens may cause the optical properties to deviate from the assumed cos⁴model. For instance, the thinner edged of the lens usually exhibits themost irregularities. Additionally, irregularities in lens shadingpatterns may also be the result of a microlens array within an imagesensor not being perfectly aligned with the color array filter. Further,the infrared (IR) filter in some lenses may cause the drop-off to beilluminant-dependent and, thus, lens shading gains may be adapteddepending upon the light source detected.

Referring to FIG. 40, a three-dimensional profile 496 depicting lightintensity versus pixel position for a typical lens is illustrated. Asshown, the light intensity near the center 498 of the lens graduallydrops off towards the corners or edges 500 of the lens. The lens shadingirregularities depicted in FIG. 40 may be better illustrated by FIG. 41,which shows a colored drawing of an image 502 that exhibits drop-offs inlight intensity towards the corners and edges. Particularly, it shouldbe noted that the light intensity at the approximate center of the imageappears to be brighter than the light intensity at the corners and/oredges of the image.

In accordance with embodiments of the present techniques, lens shadingcorrection gains may be specified as a two-dimensional grid of gains percolor channel (e.g., Gr, R, B, Gb for a Bayer filter). The gain gridpoints may be distributed at fixed horizontal and vertical intervalswithin the raw frame 278 (FIG. 19). As discussed above in FIG. 19, theraw frame 278 may include an active region 280 which defines an area onwhich processing is performed for a particular image processingoperation. With regard to the lens shading correction operation, anactive processing region, which may be referred to as the LSC region, isdefined within the raw frame region 278. As will be discussed below, theLSC region must be completely inside or at the gain grid boundaries,otherwise results may be undefined.

For instance, referring to FIG. 42, an LSC region 504 and a gain grid506 that may be defined within the raw frame 278 are shown. The LSCregion 504 may have a width 508 and a height 510, and may be defined byan x-offset 512 and a y-offset 514 with respect to the boundary of theraw frame 278. Grid offsets (e.g., grid x-offset 516 and grid y-offset518) from the base 520 of the grid gains 506 to the first pixel 522 inthe LSC region 504 is also provided. These offsets may be within thefirst grid interval for a given color component. The horizontalα-direction) and vertical (y-direction) grid point intervals 524 and526, respectively, may be specified independently for each colorchannel.

As discussed above, assuming the use of a Bayer color filter array, 4color channels of grid gains (R, B, Gr, and Gb) may be defined. In oneembodiment, a total of 4K (4096) grid points may be available, and foreach color channel, a base address for the start location of grid gainsmay be provided, such as by using a pointer. Further, the horizontal(524) and vertical (526) grid point intervals may be defined in terms ofpixels at the resolution of one color plane and, in certain embodiments,may be provide for grid point intervals separated by a power of 2, suchas by 8, 16, 32, 64, or 128, etc., in horizontal and verticaldirections. As can be appreciated, by utilizing a power of 2, efficientimplementation of gain interpolation using a shift (e.g., division) andadd operations may be achieved. Using these parameters, the same gainvalues can be used even as the image sensor cropping region is changing.For instance, only a few parameters need to be updated to align the gridpoints to the cropped region (e.g., updating the grid offsets 524 and526) instead of updating all grid gain values. By way of example only,this may be useful when cropping is used during digital zoomingoperations. Further, while the gain grid 506 shown in the embodiment ofFIG. 42 is depicted as having generally equally spaced grid points, itshould be understood that in other embodiments, the grid points may notnecessarily be equally spaced. For instance, in some embodiments, thegrid points may be distributed unevenly (e.g., logarithmically), suchthat the grid points are less concentrated in the center of the LSCregion 504, but more concentrated towards the corners of the LSC region504, typically where lens shading distortion is more noticeable.

In accordance with the presently disclosed lens shading correctiontechniques, when a current pixel location is located outside of the LSCregion 504, no gain is applied (e.g., the pixel is passed unchanged).When the current pixel location is at a gain grid location, the gainvalue at that particular grid point may be used. However, when a currentpixel location is between grid points, the gain may be interpolatedusing bi-linear interpolation. An example of interpolating the gain forthe pixel location “G” on FIG. 43 is provided below.

As shown in FIG. 43, the pixel G is between the grid points G0, G1, G2,and G3, which may correspond to the top-left, top-right, bottom-left,and bottom-right gains, respectively, relative to the current pixellocation G. The horizontal and vertical size of the grid interval isrepresented by X and Y, respectively. Additionally, ii and jj representthe horizontal and vertical pixel offsets, respectively, relative to theposition of the top left gain G0. Based upon these factors, the gaincorresponding to the position G may thus be interpolated as follows:

$\begin{matrix}{G = \frac{\begin{matrix}{\left( {G\; 0\left( {Y - {jj}} \right)\left( {X - {}} \right)} \right) + \left( {G\; 1\left( {Y - {jj}} \right)({})} \right) +} \\{\left( {G\; 2({jj})\left( {X - {}} \right)} \right) + \left( {G\; 3({})({jj})} \right)}\end{matrix}}{XY}} & \left( {13a} \right)\end{matrix}$

The terms in Equation 13a above may then be combined to obtain thefollowing expression:

$\begin{matrix}{G = \frac{\begin{matrix}{{G\; {0\left\lbrack {{XY} - {X({jj})} - {Y({})} + {({})({jj})}} \right\rbrack}} +} \\{{G\; {1\left\lbrack {{Y({})} - {({})({jj})}} \right\rbrack}} + {G\; {2\left\lbrack {{X({jj})} - {({})({jj})}} \right\rbrack}} + {G\; {3\left\lbrack {({})({jj})} \right\rbrack}}}\end{matrix}}{XY}} & \left( {13b} \right)\end{matrix}$

In one embodiment, the interpolation method may be performedincrementally, instead of using a multiplier at each pixel, thusreducing computational complexity. For instance, the term (ii)(jj) maybe realized using an adder that may be initialized to 0 at location (0,0) of the gain grid 506 and incremented by the current row number eachtime the current column number increases by a pixel. As discussed above,since the values of X and Y may be selected as powers of two, gaininterpolation may be accomplished using a simple shift operations. Thus,the multiplier is needed only at the grid point G0 (instead of at everypixel), and only addition operations are needed to determine theinterpolated gain for the remaining pixels.

In certain embodiments, the interpolation of gains between the gridpoints may use 14-bit precision, and the grid gains may be unsigned10-bit values with 2 integer bits and 8 fractional bits (e.g., 2.8floating point representation). Using this convention, the gain may havea range of between 0 and 4×, and the gain resolution between grid pointsmay be 1/256.

The lens shading correction techniques may be further illustrated by theprocess 528 shown in FIG. 44. As shown, process 528 begins at step 530,at which the position of a current pixel is determined relative to theboundaries of the LSC region 504 of FIG. 42. Next, decision logic 532determines whether the current pixel position is within the LSC region504. If the current pixel position is outside of the LSC region 504, theprocess 528 continues to step 534, and no gain is applied to the currentpixel (e.g., the pixel passes unchanged).

If the current pixel position is within the LSC region 504, the process528 continues to decision logic 536, at which it is further determinedwhether the current pixel position corresponds to a grid point withinthe gain grid 504. If the current pixel position corresponds to a gridpoint, then the gain value at that grid point is selected and applied tothe current pixel, as shown at step 538. If the current pixel positiondoes not correspond to a grid point, then the process 528 continues tostep 540, and a gain is interpolated based upon the bordering gridpoints (e.g., G0, G1, G2, and G3 of FIG. 43). For instance, theinterpolated gain may be computed in accordance with Equations 13a and13b, as discussed above. Thereafter, the process 528 ends at step 542,at which the interpolated gain from step 540 is applied to the currentpixel.

As will be appreciated, the process 528 may be repeated for each pixelof the image data. For instance, as shown in FIG. 45, athree-dimensional profile depicting the gains that may be applied toeach pixel position within a LSC region (e.g. 504) is illustrated. Asshown, the gain applied at the corners 544 of the image may be generallygreater than the gain applied to the center 546 of the image due to thegreater drop-off in light intensity at the corners, as shown in FIGS. 40and 41. Using the presently described lens shading correctiontechniques, the appearance of light intensity drop-offs in the image maybe reduced or substantially eliminated. For instance, FIG. 46 providesan example of how the colored drawing of the image 502 from FIG. 41 mayappear after lens shading correction is applied. As shown, compared tothe original image from FIG. 41, the overall light intensity isgenerally more uniform across the image. Particularly, the lightintensity at the approximate center of the image may be substantiallyequal to the light intensity values at the corners and/or edges of theimage. Additionally, as mentioned above, the interpolated gaincalculation (Equations 13a and 13b) may, in some embodiments, bereplaced with an additive “delta” between grid points by takingadvantage of the sequential column and row incrementing structure. Aswill be appreciated, this reduces computational complexity.

In further embodiments, in addition to using grid gains, a global gainper color component that is scaled as a function of the distance fromthe image center is used. The center of the image may be provided as aninput parameter, and may be estimated by analyzing the light intensityamplitude of each image pixel in the uniformly illuminated image. Theradial distance between the identified center pixel and the currentpixel, may then be used to obtain a linearly scaled radial gain, G_(r),as shown below:

G _(r) =G _(p) [c]×R,  (14)

wherein G_(p)[c] represents a global gain parameter for each colorcomponent c (e.g., R, B, Gr, and Gb components for a Bayer pattern), andwherein R represents the radial distance between the center pixel andthe current pixel.

With reference to FIG. 47, which shows the LSC region 504 discussedabove, the distance R may be calculated or estimated using severaltechniques. As shown, the pixel C corresponding to the image center mayhave the coordinates (x₀, Y₀), and the current pixel G may have thecoordinates (x_(G), Y_(G)). In one embodiment, the LSC logic 464 maycalculate the distance R using the following equation:

R=√{square root over ((x _(G) −x ₀)²+(y _(G) −y ₀)²)}{square root over((x _(G) −x ₀)²+(y _(G) −y ₀)²)}  (15)

In another embodiment, a simpler estimation formula, shown below, may beutilized to obtain an estimated value for R.

R=α×max(abs(x _(G) −x ₀),abs(y _(G) −y ₀))+β×min(abs(x _(G) −x ₀),abs(y_(G) −y ₀))  (16)

In Equation 16, the estimation coefficients α and β may be scaled to8-bit values. By way of example only, in one embodiment, a may be equalto approximately 123/128 and β may be equal to approximately 51/128 toprovide an estimated value for R. Using these coefficient values, thelargest error may be approximately 4%, with a median error ofapproximately 1.3%. Thus, even though the estimation technique may besomewhat less accurate than utilizing the calculation technique indetermining R (Equation 15), the margin of error is low enough that theestimated values or R are suitable for determining radial gaincomponents for the present lens shading correction techniques.

The radial gain G_(r) may then be multiplied by the interpolated gridgain value G (Equations 13a and 13b) for the current pixel to determinea total gain that may be applied to the current pixel. The output pixelY is obtained by multiplying the input pixel value X with the totalgain, as shown below:

Y=(G×G _(r) ×X)  (17)

Thus, in accordance with the present technique, lens shading correctionmay be performed using only the interpolated gain, both the interpolatedgain and the radial gain components. Alternatively, lens shadingcorrection may also be accomplished using only the radial gain inconjunction with a radial grid table that compensates for radialapproximation errors. For example, instead of a rectangular gain grid506, as shown in FIG. 42, a radial gain grid having a plurality of gridpoints defining gains in the radial and angular directions may beprovided. Thus, when determining the gain to apply to a pixel that doesnot align with one of the radial grid points within the LSC region 504,interpolation may be applied using the four grid points that enclose thepixel to determine an appropriate interpolated lens shading gain.

Referring to FIG. 48, the use of interpolated and radial gain componentsin lens shading correction is illustrated by the process 548. It shouldbe noted that the process 548 may include steps that are similar to theprocess 528, described above in FIG. 44. Accordingly, such steps havebeen numbered with like reference numerals. Beginning at step 530, thecurrent pixel is received and its location relative to the LSC region504 is determined. Next, decision logic 532 determines whether thecurrent pixel position is within the LSC region 504. If the currentpixel position is outside of the LSC region 504, the process 548continues to step 534, and no gain is applied to the current pixel(e.g., the pixel passes unchanged). If the current pixel position iswithin the LSC region 504, then the process 548 may continuesimultaneously to step 550 and decision logic 536. Referring first tostep 550, data identifying the center of the image is retrieved. Asdiscussed above, determining the center of the image may includeanalyzing light intensity amplitudes for the pixels under uniformillumination. This may occur during calibration, for instance. Thus, itshould be understood that step 550 does not necessarily encompassrepeatedly calculating the center of the image for processing eachpixel, but may refer to retrieving the data (e.g., coordinates) ofpreviously determined image center. Once the center of the image isidentified, the process 548 may continue to step 552, wherein thedistance between the image center and the current pixel location (R) isdetermined. As discussed above, the value of R may be calculated(Equation 15) or estimated (Equation 16). Then, at step 554, a radialgain component G_(r) may be computed using the distance R and globalgain parameter corresponding to the color component of the current pixel(Equation 14). The radial gain component G_(r) may be used to determinethe total gain, as will be discussed in step 558 below.

Referring back to decision logic 536, a determined whether the currentpixel position corresponds to a grid point within the gain grid 504. Ifthe current pixel position corresponds to a grid point, then the gainvalue at that grid point is determined, as shown at step 556. If thecurrent pixel position does not correspond to a grid point, then theprocess 548 continues to step 540, and an interpolated gain is computedbased upon the bordering grid points (e.g., G0, G1, G2, and G3 of FIG.43). For instance, the interpolated gain may be computed in accordancewith Equations 13a and 13b, as discussed above. Next, at step 558, atotal gain is determined based upon the radial gain determined at step554, as well as one of the grid gains (step 556) or the interpolatedgain (540). As can be appreciated, this may depend on which branchdecision logic 536 takes during the process 548. The total gain is thenapplied to the current pixel, as shown at step 560. Again, it should benoted that like the process 528, the process 548 may also be repeatedfor each pixel of the image data.

The use of the radial gain in conjunction with the grid gains may offervarious advantages. For instance, using a radial gain allows for the useof single common gain grid for all color components. This may greatlyreduce the total storage space required for storing separate gain gridsfor each color component. For instance, in a Bayer image sensor, the useof a single gain grid for each of the R, B, Gr, and Gb components mayreduce the gain grid data by approximately 75%. As will be appreciated,this reduction in grid gain data may decrease implementation costs, asgrid gain data tables may account for a significant portion of memory orchip area in image processing hardware. Further, depending upon thehardware implementation, the use of a single set of gain grid values mayoffer further advantages, such as reducing overall chip area (e.g., suchas when the gain grid values are stored in an on-chip memory) andreducing memory bandwidth requirements (e.g., such as when the gain gridvalues are stored in an off-chip external memory).

Having thoroughly described the functionalities of the lens shadingcorrection logic 464 shown in FIG. 37, the output of the LSC logic 464is subsequently forwarded to the inverse black level compensation (IBLC)logic 466. The IBLC logic 466 provides gain, offset and clipindependently for each color component (e.g., R, B, Gr, and Gb), andgenerally performs the inverse function to the BLC logic 462. Forinstance, as shown by the following operation, the value of the inputpixel is first multiplied by a gain and then offset by a signed value.

Y=(X×G[c])+O[c],  (18)

wherein X represents the input pixel value for a given color component c(e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for thecurrent color component c, and G[c] represents a gain value for thecolor component c. In one embodiment, the gain G[c] may have a range ofbetween approximately 0 to 4× (4 times the input pixel value X). Itshould be noted that these variables may be the same variables discussedabove in Equation 11. The computed value Y may be clipped to a minimumand maximum range using, for example, Equation 12. In one embodiment,the IBLC logic 466 may be configured to maintain a count of the numberof pixels that were clipped above and below maximum and minimum,respectively, per color component.

Thereafter, the output of the IBLC logic 466 is received by thestatistics collection block 468, which may provide for the collection ofvarious statistical data points about the image sensor(s) 90, such asthose relating to auto-exposure (AE), auto-white balance (AWB),auto-focus (AF), flicker detection, and so forth. With this in mind, adescription certain embodiments of the statistics collection block 468and various aspects related thereto is provided below with respect toFIGS. 48-66.

As will be appreciated, AWB, AE, and AF statistics may be used in theacquisition of images in digital still cameras as well as video cameras.For simplicity, AWB, AE, and AF statistics may be collectively referredto herein as “3A statistics.” In the embodiment of the ISP front-endlogic illustrated in FIG. 37, the architecture for the statisticscollection logic 468 (“3A statistics logic”) may be implemented inhardware, software, or a combination thereof. Further, control softwareor firmware may be utilized to analyze the statistics data collected bythe 3A statistics logic 468 and control various parameters of the lens(e.g., focal length), sensor (e.g., analog gains, integration times),and the ISP pipeline 82 (e.g., digital gains, color correction matrixcoefficients). In certain embodiments, the image processing circuitry 32may be configured to provide flexibility in statistics collection toenable control software or firmware to implement various AWB, AE, and AFalgorithms.

With regard to white balancing (AWB), the image sensor response at eachpixel may depend on the illumination source, since the light source isreflected from objects in the image scene. Thus, each pixel valuerecorded in the image scene is related to the color temperature of thelight source. For instance, FIG. 48 shows a graph 570 illustrating thecolor range of white areas under low color and high color temperaturesfor a YCbCr color space. As shown, the x-axis of the graph 570represents the blue-difference chroma (Cb) and the y-axis of the graph570 represents red-difference chroma (Cr) of the YCbCr color space. Thegraph 570 also shows a low color temperature axis 572 and a high colortemperature axis 574. The region 576 in which the axes 572 and 574 arepositioned, represents the color range of white areas under low and highcolor temperatures in the YCbCr color space. It should be understood,however, that the YCbCr color space is merely one example of a colorspace that may be used in conjunction with auto white balance processingin the present embodiment. Other embodiments may utilize any suitablecolor space. For instance, in certain embodiments, other suitable colorspaces may include a Lab (CIELab) color space (e.g., based on CIE 1976),a red/blue normalized color space (e.g., an R/(R+2G+B) and B/(R+2G+B)color space; a R/G and B/G color space; a Cb/Y and Cr/Y color space,etc.). Accordingly, for the purposes of this disclosure, the axes of thecolor space used by the 3A statistics logic 468 may be referred to as C1and C2 (as is the case in FIG. 49).

When a white object is illuminated under a low color temperature, it mayappear reddish in the captured image. Conversely, a white object that isilluminated under a high color temperature may appear bluish in thecaptured image. The goal of white balancing is, therefore, to adjust RGBvalues such that the image appears to the human eye as if it were takenunder canonical light. Thus, in the context of imaging statisticsrelating to white balance, color information about white objects arecollected to determine the color temperature of the light source. Ingeneral, white balance algorithms may include two main steps. First, thecolor temperature of the light source is estimated. Second, theestimated color temperature is used to adjust color gain values and/ordetermine/adjust coefficients of a color correction matrix. Such gainsmay be a combination of analog and digital image sensor gains, as wellas ISP digital gains.

For instance, in some embodiments, the imaging device 30 may becalibrated using multiple different reference illuminants. Accordingly,the white point of the current scene may be determined by selecting thecolor correction coefficients corresponding to a reference illuminantthat most closely matches the illuminant of the current scene. By way ofexample only, one embodiment may calibrate the imaging device 30 usingfive reference illuminants, a low color temperature illuminant, amiddle-low color temperature illuminant, a middle color temperatureilluminant, a middle-high color temperature illuminant, and a high colortemperature illuminant. As shown in FIG. 50, one embodiment may definewhite balance gains using the following color correction profiles:Horizon (H) (simulating a color temperature of approximately 2300degrees), Incandescent (A or IncA) (simulating a color temperature ofapproximately 2856 degrees), D50 (simulating a color temperature ofapproximately 5000 degrees), D65 (simulating a color temperature ofapproximately 6500 degrees), and D75 (simulating a color temperature ofapproximately 7500 degrees).

Depending on the illuminant of the current scene, white balance gainsmay be determined using the gains corresponding to the referenceilluminant that most closely matches the current illuminant. Forinstance, if the statistics logic 468 (described in more detail in FIG.51 below) determines that the current illuminant approximately matchesthe reference middle color temperature illuminant, D50, then whitebalance gains of approximately 1.37 and 1.23 may be applied to the redand blue color channels, respectively, while approximately no gain (1.0)is applied to the green channels (G0 and G1 for Bayer data). In someembodiments, if the current illuminant color temperature is in betweentwo reference illuminants, white balance gains may be determined viainterpolating the white balance gains between the two referenceilluminants. Further, while the present example shows an imaging devicebeing calibrated using H, A, D50, D65, and D75 illuminants, it should beunderstood that any suitable type of illuminant may be used for cameracalibration, such as TL84 or CWF (fluorescent reference illuminants),and so forth.

As will be discussed further below, several statistics may be providedfor AWB including a two-dimensional (2D) color histogram, and RGB or YCCsums to provide multiple programmable color ranges. For instance, in oneembodiment, the statistics logic 468 may provide a set of multiple pixelfilters, of which a subset of the multiple pixel filters may be selectedfor AWB processing. In one embodiment, eight sets of filters, each withdifferent configurable parameters, may be provided, and three sets ofcolor range filters may be selected from the set for gathering tilestatistics, as well as for gathering statistics for each floatingwindow. By way of example, a first selected filter may be configured tocover the current color temperature to obtain accurate color estimation,a second selected filter may be configured to cover the low colortemperature areas, and a third selected filter may be configured tocover the high color temperature areas. This particular configurationmay enable the AWB algorithm to adjust the current color temperaturearea as the light source is changing. Further, the 2D color histogrammay be utilized to determine the global and local illuminants and todetermine various pixel filter thresholds for accumulating RGB values.Again, it should be understood that the selection of three pixel filtersis meant to illustrate just one embodiment. In other embodiments, feweror more pixel filters may be selected for AWB statistics.

Further, in addition to selecting three pixel filters, one additionalpixel filter may also be used for auto-exposure (AE), which generallyrefers to a process of adjusting pixel integration time and gains tocontrol the luminance of the captured image. For instance, auto-exposuremay control the amount of light from the scene that is captured by theimage sensor(s) by setting the integration time. In certain embodiments,tiles and floating windows of luminance statistics may be collected viathe 3A statistics logic 468 and processed to determine integration andgain control parameters.

Further, auto-focus may refer to determining the optimal focal length ofthe lens in order to substantially optimize the focus of the image. Incertain embodiments, floating windows of high frequency statistics maybe collected and the focal length of the lens may be adjusted to bringan image into focus. As discussed further below, in one embodiment,auto-focus adjustments may utilize coarse and fine adjustments basedupon one or more metrics, referred to as auto-focus scores (AF scores)to bring an image into focus. Further, in some embodiments, AFstatistics/scores may be determined for different colors, and therelativity between the AF statistics/scores for each color channel maybe used to determine the direction of focus.

Thus, these various types of statistics, among others, may be determinedand collected via the statistics collection block 468. As shown, theoutput STATS0 of the statistics collection block 468 of the Sensor0statistics processing unit 120 may be sent to the memory 108 and routedto the control logic 84 or, alternatively, may be sent directly to thecontrol logic 84. Further, it should be understood that the Sensor 1statistics processing unit 122 may also include a similarly configured3A statistics collection block that provides statistics STATS 1, asshown in FIG. 8.

As discussed above, the control logic 84, which may be a dedicatedprocessor in the ISP subsystem 32 of the device 10, may process thecollected statistical data to determine one or more control parametersfor controlling the imaging device 30 and/or the image processingcircuitry 32. For instance, such the control parameters may includeparameters for operating the lens of the image sensor 90 (e.g., focallength adjustment parameters), image sensor parameters (e.g., analogand/or digital gains, integration time), as well as ISP pipe processingparameters (e.g., digital gain values, color correction matrix (CCM)coefficients). Additionally, as mentioned above, in certain embodiments,statistical processing may occur at a precision of 8-bits and, thus, rawpixel data having a higher bit-depth may be down-scaled to an 8-bitformat for statistics purposes. As discussed above, down-scaling to8-bits (or any other lower-bit resolution) may reduce hardware size(e.g., area) and also reduce processing complexity, as well as allow forthe statistics data to be more robust to noise (e.g., using spatialaveraging of the image data).

With the foregoing in mind, FIG. 51 is a block diagram depicting logicfor implementing one embodiment of the 3A statistics logic 468. Asshown, the 3A statistics logic 468 may receive a signal 582 representingBayer RGB data which, as shown in FIG. 37, may correspond to the outputof the inverse BLC logic 466. The 3A statistics logic 468 may processthe Bayer RGB data 582 to obtain various statistics 584, which mayrepresent the output STATS0 of the 3A statistics logic 468, as shown inFIG. 37, or alternatively the output STATS 1 of a statistics logicassociated with the Sensor1 statistics processing unit 122.

In the illustrated embodiment, for the statistics to be more robust tonoise, the incoming Bayer RGB pixels 582 are first averaged by the logic586. For instance, the averaging may be performed in a window size of4×4 sensor pixels consisting of four 2×2 Bayer quads (e.g., a 2×2 blockof pixels representing the Bayer pattern), and the averaged red (R),green (G), and blue (B) values in the 4×4 window may be computed andconverted to 8-bits, as mentioned above. This process is illustrates inmore detail with respect to FIG. 52, which shows a 4×4 window 588 ofpixels formed as four 2×2 Bayer quads 590. Using this arrangement, eachcolor channel includes a 2×2 block of corresponding pixels within thewindow 588, and same-colored pixels may be summed and averaged toproduce an average color value for each color channel within the window588. For instance, red pixels 594 may be averaged to obtain an averagered value (R_(AV)) 604, and the blue pixels 596 may be averaged toobtain an average blue value (B_(AV)) 606 within the sample 588. Withregard to averaging of the green pixels, several techniques may beutilized since the Bayer pattern has twice as many green samples as redor blue samples. In one embodiment, the average green value (G_(AV)) 602may be obtained by averaging just the Gr pixels 592, just the Gb pixels598, or all of the Gr and Gb pixels 592 and 598 together. In anotherembodiment, the Gr and Gb pixels 592 and 598 in each Bayer quad 590 maybe averaged, and the average of the green values for each Bayer quad 590may be further averaged together to obtain G_(AV) 602. As will beappreciated, the averaging of the pixel values across pixel blocks mayprovide for the reduction of noise. Further, it should be understoodthat the use of a 4×4 block as a window sample is merely intended toprovide one example. Indeed, in other embodiments, any suitable blocksize may be utilized (e.g., 8×8, 16×16, 32×32, etc.).

Thereafter, the down-scaled Bayer RGB values 610 are input to the colorspace conversion logic units 612 and 614. Because some of the 3Astatistics data may rely upon pixel pixels after applying color spaceconversion, the color space conversion (CSC) logic 612 and CSC logic 614may be configured to convert the down-sampled Bayer RGB values 610 intoone or more other color spaces. In one embodiment, the CSC logic 612 mayprovide for a non-linear space conversion and the CSC logic 614 mayprovide for a linear space conversion. Thus, the CSC logic units 612 and614 may convert the raw image data from sensor Bayer RGB to anothercolor space (e.g., sRGB_(linear), sRGB, YCbCr, etc.) that may be moreideal or suitable for performing white point estimation for whitebalance.

In the present embodiment, the non-linear CSC logic 612 may beconfigured to perform a 3×3 matrix multiply, followed by a non-linearmapping implemented as a lookup table, and further followed by another3×3 matrix multiply with an added offset. This allows for the 3Astatistics color space conversion to replicate the color processing ofthe RGB processing in the ISP pipeline 82 (e.g., applying white balancegain, applying a color correction matrix, applying RGB gammaadjustments, and performing color space conversion) for a given colortemperature. It may also provide for the conversion of the Bayer RGBvalues to a more color consistent color space such as CIELab, or any ofthe other color spaces discussed above (e.g., YCbCr, a red/bluenormalized color space, etc.). Under some conditions, a Lab color spacemay be more suitable for white balance operations because thechromaticity is more linear with respect to brightness.

As shown in FIG. 51, the output pixels from the Bayer RGB down-scaledsignal 610 are processed with a first 3×3 color correction matrix(3A_CCM), referred to herein by reference number 614. In the presentembodiment, the 3A_CCM 616 may be configured to convert from a cameraRGB color space (camRGB), to a linear sRGB calibrated space(sRGB_(linear)). A programmable color space conversion that may be usedin one embodiment is provided below by Equations 19-21:

sR_(linear)=max(0,min(255,(3A_CCM_(—)00*R+3A_CCM_(—)01*G+3A_CCM_(—)02*B)));  (19)

sG_(linear)=max(0,min(255,(3A_CCM_(—)10*R+3A_CCM_(—)11*G+3A_CCM_(—)12*B)));  (20)

sB_(linear)=max(0,min(255,(3A_CCM_(—)20*R+3A_CCM_(—)21*G+3A_CCM_(—)22*B)));  (21)

wherein 3A_CCM_00-3A_CCM_22 represent signed coefficients of the matrix614. Thus, each of the sR_(linear), sG_(linear), and sB_(linear),components of the sRGB_(linear) color space may be determined firstdetermining the sum of the red, blue, and green down-sampled Bayer RGBvalues with corresponding 3A_CCM coefficients applied, and then clippingthis value to either 0 or 255 (the minimum and maximum pixel values for8-bit pixel data) if the value exceeds 255 or is less than 0. Theresulting sRGB_(linear) values are represented in FIG. 51 by referencenumber 618 as the output of the 3A_CCM 616. Additionally, the 3Astatistics logic 468 may maintain a count of the number of clippedpixels for each of the sR_(linear), sG_(linear), and sB_(linear)components, as expressed below:

3A_CCM_R_clipcount_low: number of sR_(linear) pixels<0 clipped

3A_CCM_R_clipcount_high: number of sR_(linear) pixels>255 clipped

3A_CCM_G_clipcount_low: number of sG_(linear) pixels<0 clipped

3A_CCM_G_clipcount_high: number of sG_(linear) pixels>255 clipped

3A_CCM_B_clipcount_low: number of sB_(linear) pixels<0 clipped

3A_CCM_B_clipcount_high: number of sB_(linear) pixels>255 clipped

Next, the sRGB_(linear) pixels 618 may be processed using a non-linearlookup table 620 to produce sRGB pixels 622. The lookup table 620 maycontain entries of 8-bit values, with each table entry valuerepresenting an output levels. In one embodiment, the look-up table 620may include 65 evenly distributed input entries, wherein a table indexrepresents input values in steps of 4. When the input value fallsbetween intervals, the output values are linearly interpolated.

As will be appreciated, the sRGB color space may represent the colorspace of the final image produced by the imaging device 30 (FIG. 7) fora given white point, as white balance statistics collection is performedin the color space of the final image produced by the image device. Inone embodiment, a white point may be determined by matching thecharacteristics of the image scene to one or more reference illuminantsbased, for example, upon red-to-green and/or blue-to-green ratios. Forinstance, one reference illuminant may be D65, a CIE standard illuminantfor simulating daylight conditions. In addition to D65, calibration ofthe imaging device 30 may also be performed for other differentreference illuminants, and the white balance determination process mayinclude determining a current illuminant so that processing (e.g., colorbalancing) may be adjusted for the current illuminant based oncorresponding calibration points. By way of example, in one embodiment,the imaging device 30 and 3A statistics logic 468 may be calibratedusing, in addition to D65, a cool white fluorescent (CWF) referenceilluminant, the TL84 reference illuminant (another fluorescent source),and the IncA (or A) reference illuminant, which simulates incandescentlighting. Additionally, as discussed above, various other illuminantscorresponding to different color temperatures (e.g., H, IncA, D50, D65,and D75, etc.) may also be used in camera calibration for white balanceprocessing. Thus, a white point may be determined by analyzing an imagescene and determining which reference illuminant most closely matchesthe current illuminant source.

Referring still to the non-linear CSC logic 612, the sRGB pixel output620 of the look-up table 620 may be further processed with a second 3×3color correction matrix 624, referred to herein as 3A_CSC. In thedepicted embodiment, the 3A_CSC matrix 624 is shown as being configuredto convert from the sRGB color space to the YCbCr color space, though itmay be configured to convert the sRGB values into other color spaces aswell. By way of example, the following programmable color spaceconversion (Equations 22-27) may be used:

Y=3A_CSC_(—)00*sR+3A_CSC_(—)01*sG+3A_CSC_(—)02*sB+3A_OffsetY;  (22)

Y=max(3A_CSC_MIN_(—) Y,min(3A_CSC_MAX_(—) Y,Y));  (23)

C1=3A_CSC_(—)10*sR+3A_CSC_(—)11*sG+3A_CSC_(—)12*sB+3A_OffsetC1;  (24)

C1=max(3A_CSC_MIN_(—) C1,min(3A_CSC_MAX_(—) C1,C1));  (25)

C2=3A_CSC_(—)20*sR+3A_CSC_(—)21*sG+3A_CSC_(—)22*sB+3A_OffsetC2;  (26)

C2=max(3A_CSC_MIN_(—) C2,min(3A_CSC_MAX_(—) C2,C2));  (27)

wherein 3A_CSC_00-3A_CSC_22 represent signed coefficients for the matrix624, 3A_OffsetY, 3A_OffsetC1, and 3A_OffsetC2 represent signed offsets,and C1 and C2 represent different colors, here blue-difference chroma(Cb) and red-difference chroma (Cr), respectively. It should beunderstood, however, that C1 and C2 may represent any suitabledifference chroma colors, and need not necessarily be Cb and Cr colors.

As shown in Equations 22-27, in determining each component of YCbCr,appropriate coefficients from the matrix 624 are applied to the sRGBvalues 622 and the result is summed with a corresponding offset (e.g.,Equations 22, 24, and 26). Essentially, this step is a 3×1 matrixmultiplication step. This result from the matrix multiplication is thenclipped between a maximum and minimum value (e.g., Equations 23, 25, and27). The associated minimum and maximum clipping values may beprogrammable and may depend, for instance, on particular imaging orvideo standards (e.g., BT.601 or BT.709) being utilized.

The 3A statistics logic 468 may also maintain a count of the number ofclipped pixels for each of the Y, C1, and C2 components, as expressedbelow:

3A_CSC_Y_clipcount_low: number of Y pixels<3A_CSC_MIN_Y clipped

-   -   3A_CSC_Y_clipcount_high: number of Y pixels>3A_CSC_MAX_Y clipped    -   3A_CSC_C1_clipcount_low: number of C1 pixels<3A_CSC_MIN_C1        clipped    -   3A_CSC_C1_clipcount_high: number of C1 pixels>3A_CSC_MAX_C1        clipped    -   3A_CSC_C2_clipcount_low: number of C2 pixels<3A_CSC_MIN_C2        clipped

The output pixels from the Bayer RGB down-sample signal 610 may also beprovided to the linear color space conversion logic 614, which may beconfigured to implement a camera color space conversion. For instance,the output pixels 610 from the Bayer RGB down-sample logic 586 may beprocessed via another 3×3 color conversion matrix (3A_CSC2) 630 of theCSC logic 614 to convert from sensor RGB (camRGB) to a linearwhite-balanced color space (camYC1C2), wherein C1 and C2 may correspondto Cb and Cr, respectively. In one embodiment, the chroma pixels may bescaled by luma, which may be beneficial in implementing a color filterthat has improved color consistency and is robust to color shifts due toluma changes. An example of how the camera color space conversion may beperformed using the 3×3 matrix 630 is provided below in Equations 28-31:

camY=3A_CSC2_(—)00*R+3A_CSC2_(—)01*G+3A_CSC2_(—)02*B+3A_Offset2Y;  (28)

camY=max(3A_CSC2_MIN_(—) Y,min(3A_CSC2_MAX_(—) Y,camY));  (29)

camC1=(3A_CSC2_(—)10*R+3A_CSC2_(—)11*G+3A_CSC2_(—)12*B);  (30)

camC2=(3A_CSC2_(—)20*R+3A_CSC2_(—)21*G+3A_CSC2_(—)22*B);  (31)

wherein 3A_CSC2_00-3A_CSC2_22 represent signed coefficients for thematrix 630, 3A_Offset2Y represents a signed offset for camY, and camC1and camC2 represent different colors, here blue-difference chroma (Cb)and red-difference chroma (Cr), respectively. As shown in Equation 28,to determine camY, corresponding coefficients from the matrix 630 areapplied to the bayer RGB values 610, and the result is summed with3A_Offset2Y. This result is then clipped between a maximum and minimumvalue, as shown in Equation 29. As discussed above, the clipping limitsmay be programmable.

At this point, the camC1 and camC2 pixels of the output 632 are signed.As discussed above, in some embodiments, chroma pixels may be scaled.For example, one technique for implementing chroma scaling is shownbelow:

camC1=camC1*ChromaScale*255/(camY?camY:1);  (32)

camC2=camC2*ChromaScale*255/(camY?camY:1);  (33)

wherein ChromaScale represents a floating point scaling factor between 0and 8. In Equations 32 and 33, the expression (camY ?camY:1) is meant toprevent a divide-by-zero condition. That is, if camY is equal to zero,the value of camY is set to 1. Further, in one embodiment, ChromaScalemay be set to one of two possible values depending on the sign of camC1.For instance, as shown below in Equation 34, ChomaScale may be set to afirst value (ChromaScale0) if camC1 is negative, or else may be set to asecond value (ChromaScale1):

$\begin{matrix}{{ChromaScale} = \begin{matrix}{{ChromaScale}\; 0} & {{if}\left( {{{camC}\; 1} < 0} \right)} \\{{ChromaScale}\; 1} & {otherwise}\end{matrix}} & (34)\end{matrix}$

Thereafter, chroma offsets are added, and the camC1 and camC2 chromapixels are clipped, as shown below in Equations 35 and 36, to generatecorresponding unsigned pixel values:

camC1=max(3A_CSC2_MIN_(—) C1,min(3A_CSC2_MAX_(—)C1,(camC1+3A_Offset2C1)))  (35)

camC2=max(3A_CSC2_MIN_(—) C2,min(3A_CSC2_MAX_(—)C2,(camC2+3A_Offset2C2)))  (36)

wherein 3A_CSC2_00-3A_CSC2_22 are signed coefficients of the matrix 630,and 3A_Offset2C1 and 3A_Offset2C2 are signed offsets. Further, thenumber of pixels that are clipped for camY, camC1, and camC2 arecounted, as shown below:

3A_CSC2_Y_clipcount_low: number of camY pixels<3A_CSC2_MIN_Y clipped

3A_CSC2_Y_clipcount_high: number of camY pixels>3A_CSC2_MAX_Y clipped

3A_CSC2_C1_clipcount_low: number of camC1 pixels<3A_CSC2_MIN_C1 clipped

3A_CSC2_C1_clipcount_high: number of camC1 pixels>3A_CSC2_MAX_C1 clipped

3A_CSC2_C2_clipcount_low: number of camC2 pixels<3A_CSC2_MIN_C2 clipped

3A_CSC2_C2_clipcount_high: number of camC2 pixels>3A_CSC2_MAX_C2 clipped

Thus, the non-linear and linear color space conversion logic 612 and 614may, in the present embodiment, provide pixel data in various colorspaces: sRGB_(linear) (signal 618), sRGB (signal 622), YCbYr (signal626), and camYCbCr (signal 630). It should be understood that thecoefficients for each conversion matrix 616 (3A_CCM), 624 (3A_CSC), and630 (3A_CSC2), as well as the values in the look-up table 620, may beindependently set and programmed

Referring still to FIG. 51, the chroma output pixels from either thenon-linear color space conversion (YCbCr 626) or the camera color spaceconversion (camYCbCr 632) may be used to generate a two-dimensional (2D)color histogram 636. As shown, selection logic 638 and 640, which may beimplemented as multiplexers or by any other suitable logic, may beconfigured to select between luma and chroma pixels from either thenon-linear or camera color space conversion. The selection logic 638 and640 may operate in response to respective control signals which, in oneembodiment, may be supplied by the main control logic 84 of the imageprocessing circuitry 32 (FIG. 7) and may be set via software.

For the present example, it may be assumed that the selection logic 638and 640 select the YC1C2 color space conversion (626), where the firstcomponent is Luma, and where C1, C2 are the first and second colors(e.g., Cb, Cr). A 2D histogram 636 in the C1-C2 color space is generatedfor one window. For instance, the window may be specified with a columnstart and width, and a row start and height. In one embodiment, thewindow position and size may be set as a multiple of 4 pixels, and 32×32bins may be used for a total of 1024 bins. The bin boundaries may be atfixed interval and, in order to allow for zooming and panning of thehistogram collection in specific areas of the color space, a pixelscaling and offset may defined.

The upper 5 bits (representing a total of 32 values) of C1 and C2 afteroffset and scaling may used to determine the bin. The bin indices for C1and C2, referred to herein by C1 index and C2 index, may be determinedas follows:

C1_index=((C1−C1_offset)>>(3−C1_scale)  (37)

C2_index=((C2−C2_offset)>>(3−C2_scale)  (38)

Once the indices are determined, the color histogram bins areincremented by a Count value (which may have a value of between 0 and 3in one embodiment) if the bin indices are in the range [0, 31], as shownbelow in Equation 39. Effectively, this allows for weighting the colorcounts based on luma values (e.g., brighter pixels are weighted moreheavily, instead of weighting everything equally (e.g., by 1)).

$\begin{matrix}{{{if}\begin{pmatrix}{{{C\; 1{\_ index}}>=0}\&\&{{C\; 1{\_ index}}<=31}\&\&} \\{{{C\; 2{\_ index}}>=0}\&\&{{C\; 2{\_ index}}<=31}}\end{pmatrix}}{{{{{StatsCbCrHist}\left\lbrack {{{C\; 2{\_ index}}\&}31} \right\rbrack}\left\lbrack {{{C\; 1{\_ index}}\&}31} \right\rbrack}+={Count}};}} & (39)\end{matrix}$

where Count is determined based on the selected luma value, Y in thisexample. As will be appreciated, the steps represented by Equations 37,38, and 39 may be implemented by a bin update logic block 644. Further,in one embodiment, multiple luma thresholds may be set to define lumaintervals. By way of example, four luma thresholds (Ythd0-Ythd3) maydefine five luma intervals, with Count values Count0-4 being defined foreach interval. For instance, Count0-Count4 may be selected (e.g., bypixel condition logic 642) based on luma thresholds as follows:

if (Y<=Ythd0)

Count=Count0

else if (Y<=Ythd1)

Count=Count1

else if (Y<=Ythd2)

Count=Count2

else if (Y<=Ythd3)

Count=Count3

else

Count=Count4  (40)

With the foregoing in mind, FIG. 53 illustrates the color histogram withscaling and offsets set to zero for both C1 and C2. The divisions withinthe CbCr space represent each of the 32×32 bins (1024 total bins). FIG.54 provides an example of zooming and panning within the 2D colorhistogram for additional precision, wherein the rectangular area 646where the small rectangle specifies the location of the 32×32 bins.

At the start of a frame of image data, bin values are initialized tozero. For each pixel going into the 2D color histogram 636, the bincorresponding to the matching C1C2 value is incremented by a determinedCount value (Count0-Count4) which, as discussed above, may be based onthe luma value. For each bin within the 2D histogram 636, the totalpixel count is reported as part of the collected statistics data (e.g.,STATS0). In one embodiment, the total pixel count for each bin may havea resolution of 22-bits, whereby an allocation of internal memory equalto 1024×22 bits is provided.

Referring back to FIG. 51, the Bayer RGB pixels (signal 610),sRGB_(linear) pixels (signal 618), sRGB pixels (signal 622), and YC1C2(e.g., YCbCr) pixels (signal 626) are provided to a set of pixel filters650 a-c, where by RGB, sRGB_(linear), sRGB, YC1C2, or camYC1C2 sums maybe accumulated conditionally upon either camYC1C2 or YC pixelconditions, as defined by each pixel filter 650. That is, Y, C1 and C2values from either output of the non-linear color space conversion(YC1C2) or the output of the camera color space conversion (camYC1C2)are used to conditionally select RGB, sRGB_(linear), sRGB or YC1C2values to accumulate. While the present embodiment depicts the 3Astatistics logic 468 as having 8 pixel filters (PF0-PF7) provided, itshould be understood that any number of pixel filters may be provided.

FIG. 55 shows a functional logic diagram depicting an embodiment of thepixel filters, specifically PF0 (650 a) and PF1 (650 b) from FIG. 51. Asshown, each pixel filter 650 includes a selection logic, which receivesthe Bayer RGB pixels, the sRGB_(linear) pixels, the sRGB pixels, and oneof either the YC1C2 or camYC1C2 pixels, as selected by another selectionlogic 654. By way of example, the selection logic 652 and 654 may beimplemented using multiplexers or any other suitable logic. Theselection logic 654 may select either YC1C2 or camYC1C2. The selectionmay be made in response to a control signal which may be supplied by themain control logic 84 of the image processing circuitry 32 (FIG. 7)and/or set by software. Next, the pixel filter 650 may use logic 656 toevaluate the YC1C2 pixels (e.g., either non-linear or camera) selectedby the selection logic 654 against a pixel condition. Each pixel filter650 may use the selection circuit 652 to select one of either the BayerRGB pixels, sRGB_(linear) pixels, sRGB pixels, and YC1C2 or camYC1C2pixel depending on the output from the selection circuit 654.

Using the results of the evaluation, the pixels selected by theselection logic 652 may be accumulated. In one embodiment, the pixelcondition may be defined using thresholds C1_min, C1_max, C2_min,C2_max, as shown in graph 570 of FIG. 49. A pixel is included in thestatistics if it satisfies the following conditions:

1. C1_min<=C1<=C1_max

2. C2_min<=C2<=C2_max

3. abs((C2_delta*C1)−(C1_delta*C2)+Offset)<distance_max

4. Y_(min)<=Y<=Y_(max)

Referring to graph FIG. 56, in one embodiment, the point 662 representsthe values (C2, C1) corresponding to the current YC1C2 pixel data, asselected by the logic 654. C1_delta may be determined as the differencebetween C1_1 and C1_0, and C2_delta may be determined as the differencebetween C2_1 and C2_0. As shown in FIG. 56, the points (C1_0, C2_0) and(C1_1, C2_1) may define the minimum and maximum boundaries for C1 andC2. The Offset may be determined by multiplying C1_delta by the value(C2_intercept) at where the line 664 intercepts the axis C2. Thus,assuming that Y, C1, and C2 satisfy the minimum and maximum boundaryconditions, the selected pixels (Bayer RGB, sRGB_(linear), sRGB, andYC1C2/camYC1C2) is included in the accumulation sum if its distance 670from the line 664 is less than distance_max 672, which may be distance670 in pixels from the line multiplied by a normalization factor:

distance_max=distance*sqrt(C1_deltâ2+C2_deltâ2)

In the present embodiment, distance, C1_delta and C2_delta may have arange of −255 to 255. Thus, distance_max 672 may be represented by 17bits. The points (C1_0, C2_0) and (C1_1, C2_1), as well as parametersfor determining distance_max (e.g., normalization factor(s)), may beprovided as part of the pixel condition logic 656 in each pixel filter650. As will be appreciated, the pixel conditions 656 may beconfigurable/programmable.

While the example shown in FIG. 56 depicts a pixel condition based ontwo sets of points (C1_0, C2_0) and (C1_1, C2_1), in additionalembodiments, certain pixel filters may define more complex shapes andregions upon which pixel conditions are determined. For instance, FIG.57 shows an embodiment where a pixel filter 650 may define a five-sidedpolygon 673 using points (C1_0, C2_0), (C1_1, C2_1), (C1_2, C2_2) and(C1_3, C2_3), and (C1_4, C2_4). Each side 674 a-674 e may define a linecondition. However, unlike the case shown in FIG. 56 (e.g., the pixelmay be on either side of line 664 as long as distance_max is satisfied),the condition may be that the pixel (C1, C2) must be located on the sideof the line 674 a-674 e such that it is enclosed by the polygon 673.Thus, the pixel (C1, C2) is counted when the intersection of multipleline conditions is met. For instance, in FIG. 57, such an intersectionoccurs with respect to pixel 675 a. However, pixel 675 b fails tosatisfy the line condition for line 674 d and, therefore, would not becounted in the statistics when processed by a pixel filter configured inthis manner.

In a further embodiment, shown in FIG. 58, a pixel condition may bedetermined based on overlapping shapes. For instance, FIG. 58 shows howa pixel filter 650 may have pixel conditions defined using twooverlapping shapes, here rectangles 676 a and 676 b defined by points(C1_0, C2_0), (C1_1, C2_1), (C1_2, C2_2) and (C1_3, C2_3) and points(C1_4, C2_4), (C1_5, C2_5), (C1_6, C2_6) and (C1_7, C2_7), respectively.In this example, a pixel (C1, C2) may satisfy line conditions defined bysuch a pixel filter by being enclosed within the region collectivelybounded by the shapes 676 a and 676 b (e.g., by satisfying the lineconditions of each line defining both shapes). For instance, in FIG. 58,these conditions are satisfied with respect to pixel 678 a. However,pixel 678 b fails to satisfy these conditions (specifically with respectto line 679 a of rectangle 676 a and line 679 b of rectangle 676 b) and,therefore, would not be counted in the statistics when processed by apixel filter configured in this manner.

For each pixel filter 650, qualifying pixels are identified based on thepixel conditions defined by logic 656 and, for qualifying pixel values,the following statistics may be collected by the 3A statistics engine468: 32-bit sums: (R_(sum), G_(sum), B_(sum)) or (sR_(linear) _(—)_(sum), sG_(linear) _(—) _(sum), sB_(linear) _(—) _(sum)), or (sR_(sum),sG_(sum), sB_(sum)) or (Y_(sum), C1 _(sum), C2 _(sum)) and a 24-bitpixel count, Count, which may represent the sum of the number of pixelsthat were included in the statistic. In one embodiment, software may usethe sum to generate an average in within a tile or window.

When the camYC1C2 pixels are selected by logic 652 of a pixel filter650, color thresholds may be performed on scaled chroma values. Forinstance, since chroma intensity at the white points increases with lumavalue, the use of chroma scaled with the luma value in the pixel filter650 may, in some instances, provide results with improved consistency.For example, minimum and maximum luma conditions may allow the filter toignore dark and/or bright areas. If the pixel satisfies the YC1C2 pixelcondition, the RGB, sRGB_(linear), sRGB or YC1C2 values are accumulated.The selection of the pixel values by the selection logic 652 may dependon the type of information needed. For instance, for white balance,typically RGB or sRGB_(linear) pixels are selected. For detectingspecific conditions, such as sky, grass, skin tones, etc., a YCC or sRGBpixel set may be more suitable.

In the present embodiment, eight sets of pixel conditions may bedefined, one associated with each of the pixel filters PF0-PF7 650. Somepixel conditions may be defined to carve an area in the C1-C2 colorspace (FIG. 49) where the white point is likely to be. This may bedetermined or estimated based on the current illuminant. Then,accumulated RGB sums may be used to determine the current white pointbased on the R/G and/or B/G ratios for white balance adjustments.Further, some pixel conditions may be defined or adapted to performscene analysis and classifications. For example, some pixel filters 650and windows/tiles may be utilized to detect for conditions, such as bluesky in a top portion of an image frame, or green grass in a bottomportion of an image frame. This information can also be used to adjustwhite balance. Additionally, some pixel conditions may be defined oradapted to detect skin tones. For such filters, tiles may be used todetect areas of the image frame that have skin tone. By identifyingthese areas, the quality of skin tone may be improved by, for example,reducing the amount of noise filter in skin tone areas and/or decreasingthe quantization in the video compression in those areas to improvequality.

The 3A statistics logic 468 may also provide for the collection of lumadata. For instance, the luma value, camY, from the camera color spaceconversion (camYC1C2) may be used for accumulating luma sum statistics.In one embodiment, the following luma information is may be collected bythe 3A statistics logic 468:

Y_(sum): sum of camY

cond(Y_(sum)): sum of camY that satisfies the condition:Y_(min)<=camY<Y_(max)

Ycount1: count of pixels where camY<Y_(min),

Ycount2: count of pixels where camY>=Y_(max)

Here, Ycount1 may represent the number of underexposed pixels andYcount2 may represent the number of overexposed pixels. This may be usedto determine whether the image is overexposed or underexposed. Forinstance, if the pixels do not saturate, the sum of camY (Y_(sum)) mayindicate average luma in a scene, which may be used to achieve a targetAE exposure. For instance, in one embodiment, the average luma may bedetermined by dividing Y_(sum) by the number of pixels. Further, byknowing the luma/AE statistics for tile statistics and window locations,AE metering may be performed. For instance, depending on the imagescene, it may be desirable to weigh AE statistics at the center windowmore heavily than those at the edges of the image, such as may be in thecase of a portrait.

In the presently illustrated embodiment, the 3A statistics collectionlogic may be configured to collect statistics in tiles and windows. Inthe illustrated configuration, one window may be defined for tilestatistics 674. The window may be specified with a column start andwidth, and a row start and height. In one embodiment, the windowposition and size may be selected as a multiple of four pixels and,within this window, statistics are gathered in tiles of arbitrary sizes.By way of example, all tiles in the window may be selected such thatthey have the same size. The tile size may be set independently forhorizontal and vertical directions and, in one embodiment, the maximumlimit on the number of horizontal tiles may be set (e.g., a limit of 128horizontal tiles). Further, in one embodiment, the minimum tile size maybe set to 8 pixels wide by 4 pixels high, for example. Below are someexamples of tile configurations based on different video/imaging modesand standards to obtain a window of 16×16 tiles:

VGA 640×480: the interval 40×30 pixels

HD1280×720: the interval 80×45 pixels

HD1920×1080: the interval 120×68 pixels

5 MP 2592×1944: the interval 162×122 pixels

8 MP 3280×2464: the interval 205×154 pixels

With regard to the present embodiment, from the eight available pixelfilters 650 (PF0-PF7), four may be selected for tile statistics 674. Foreach tile, the following statistics may collected:

-   -   (R_(sum0), G_(sum0), B_(sum0)) or (sR_(linear) _(—) _(sum0),        sG_(linear) _(—) _(sum0), sB_(linear) _(—) _(sum0)), or        (sR_(sum0), sG_(sum0), sB_(sum0)) or (Y_(sum0), C1 _(sum0), C2        _(sum0)), Count0    -   (R_(sum1), G_(sum1), B_(sum1)) or (sR_(linear) _(—) _(sum1),        sG_(linear) _(—) _(sum1), sB_(linear) _(—) _(sum1)), or        (sR_(sum1), sG_(sum1), sB_(sum1)) or (Y_(sum1), C1 _(sum1), C2        _(sum1)), Count1    -   (R_(sum2), G_(sum2), B_(sum2)) or (sR_(linear) _(—) _(sum2),        sG_(linear) _(—) _(sum2), sB_(linear) _(—) _(sum2)), or        (sR_(sum2), sG_(sum2), sB_(sum2)) or (Y_(sum2), C1 _(sum2), C2        _(sum2)), Count2    -   (R_(sum3), G_(sum3), B_(sum3)) or (sR_(linear) _(—) _(sum3),        sG_(linear) _(—) _(sum3), sB_(linear) _(—) _(sum3)), or        (sR_(sum3), sG_(sum3), sB_(sum3)) or (Y_(sum3), C1 _(sum3), C2        _(sum3)), Count3    -   Y_(sum), cond(Y_(sum)), Y_(count1), Y_(count2) (from camY)        In the above-listed statistics, Count0-3 represents the count of        pixels that satisfy pixel conditions corresponding to the        selected four pixel filters. For example, if pixel filters PF0,        PF1, PF5, and PF6 are selected as the four pixel filters for a        particular tile or window, then the above-provided expressions        may correspond to the Count values and sums corresponding to the        pixel data (e.g., Bayer RGB, sRGB_(linear), sRGB, YC1Y2,        camYC1C2) which is selected for those filters (e.g., by        selection logic 652). Additionally, the Count values may be used        to normalize the statistics (e.g., by dividing color sums by the        corresponding Count values). As shown, depending at least        partially upon the types of statistics needed, the selected        pixels filters 650 may be configured to select between either        one of Bayer RGB, sRGB_(linear), or sRGB pixel data, or YC1C2        (non-linear or camera color space conversion depending on        selection by logic 654) pixel data, and determine color sum        statistics for the selected pixel data. Additionally, as        discussed above the luma value, camY, from the camera color        space conversion (camYC1C2) is also collected for luma sum        information for auto-exposure (AE) statistics.

Additionally, the 3A statistics logic 468 may also be configured tocollect statistics 676 for multiple windows. For instance, in oneembodiment, up to eight floating windows may be used, with anyrectangular region having a multiple of four pixels in each dimension(e.g., height×width), up to a maximum size corresponding to the size ofthe image frame. However, the location of the windows is not necessarilyrestricted to multiples of four pixels. For instance, windows canoverlap with one another.

In the present embodiment, four pixel filters 650 may be selected fromthe available eight pixel filters (PF0-PF7) for each window. Statisticsfor each window may be collected in the same manner as for tiles,discussed above. Thus, for each window, the following statistics 676 maybe collected:

-   -   (R_(sum0), G_(sum0), B_(sum0)) or (sR_(linear) _(—) _(sum0),        sG_(linear) _(—) _(sum0), sB_(linear) _(—) _(sum0)), or        (sR_(sum0), sG_(sum0), sB_(sum0)) or (Y_(sum0), C1 _(sum0), C2        _(sum0)), Count0    -   (R_(sum1), G_(sum1), B_(sum1)) or (sR_(linear) _(—) _(sum1),        sG_(linear) _(—) _(sum1), sB_(linear) _(—) _(sum1)), or        (sR_(sum1), sG_(sum1), sB_(sum1)) or (Y_(sum1), C1 _(sum1), C2        _(sum1)), Count1    -   (R_(sum2), G_(sum2), B_(sum2)) or (sR_(linear) _(—) _(sum2),        sG_(linear) _(—) _(sum2), sB_(linear) _(—) _(sum2)), or        (sR_(sum2), sG_(sum2), sB_(sum2)) or (Y_(sum2), C1 _(sum2), C2        _(sum2)), Count2    -   (R_(sum3), G_(sum3), B_(sum3)) or (sR_(linear) _(—) _(sum3),        sG_(linear) _(—) _(sum3), sB_(linear) _(—) _(sum3)), or        (sR_(sum3), sG_(sum3), sB_(sum3)) or (Y_(sum3), C1 _(sum3), C2        _(sum3)), Count3    -   Y_(sum), cond(Y_(sum)), Y_(count1), Y_(count2) (from camY)        In the above-listed statistics, Count0-3 represents the count of        pixels that satisfy pixel conditions corresponding to the        selected four pixel filters for a particular window. From the        eight available pixel filters PF0-PF7, the four active pixel        filters may be selected independently for each window.        Additionally, one of the sets of statistics may be collected        using pixel filters or the camY luma statistics. The window        statistics collected for AWB and AE may, in one embodiment, be        mapped to one or more registers.

Referring still to FIG. 51, the 3A statistics logic 468 may also beconfigured to acquire luma row sum statistics 678 for one window usingthe luma value, camY, for the camera color space conversion. Thisinformation may be used to detect and compensate for flicker. Flicker isgenerated by a periodic variation in some fluorescent and incandescentlight sources, typically caused by the AC power signal. For example,referring to FIG. 59, a graph illustrating how flicker may be caused byvariations in a light source is shown. Flicker detection may thus beused to detect the frequency of the AC power used for the light source(e.g., 50 Hz or 60 Hz). Once the frequency is known, flicker may beavoided by setting the image sensor's integration time to an integermultiple of the flicker period.

To detect for flicker, the camera luma, camY, is accumulated over eachrow. Due to the down-sample of the incoming Bayer data, each camY valuemay corresponds to 4 rows of the original raw image data. Control logicand/or firmware may then perform a frequency analysis of the row averageor, more reliably, of the row average differences over consecutiveframes to determine the frequency of the AC power associated with aparticular light source. For example, with respect to FIG. 59,integration times for the image sensor may be based on times t1, t2, t3,and t4 (e.g., such that integration occurs at times corresponding towhen a lighting source exhibiting variations is generally at the samebrightness level.

In one embodiment, a luma row sum window may be specified and statistics678 are reported for pixels within that window. By way of example, for1080p HD video capture, assuming a window of 1024 pixel high, 256 lumarow sums are generated (e.g., one sum for every four rows due todownscaling by logic 586), and each accumulated value may be expressedwith 18 bits (e.g., 8-bit camY values for up to 1024 samples per row).

The 3A statistics collection logic 468 of FIG. 51 may also provide forthe collection of auto-focus (AF) statistics 682 by way of theauto-focus statistics logic 680. A functional block diagram showing anembodiment of the AF statistics logic 680 in more detail is provided inFIG. 60. As shown, the AF statistics logic 680 may include a horizontalfilter 684 and an edge detector 686 which is applied to the originalBayer RGB (not down-sampled), two 3×3 filters 688 on Y from Bayer, andtwo 3×3 filters 690 on camY. In general, the horizontal filter 684provides a fine resolution statistics per color component, the 3×3filters 688 may provide fine resolution statistics on BayerY (Bayer RGBwith 3×1 transform (logic 687) applied), and the 3×3 filters 690 mayprovide coarser two-dimensional statistics on camY (since camY isobtained using down-scaled Bayer RGB data, i.e., logic 630). Further,the logic 680 may include logic 704 for decimating the Bayer RGB data(e.g., 2×2 averaging, 4×4 averaging, etc.), and the decimated Bayer RGBdata 705 may be filtered using 3×3 filters 706 to produce a filteredoutput 708 for decimated Bayer RGB data. The present embodiment providesfor 16 windows of statistics. At the raw frame boundaries, edge pixelsare replicated for the filters of the AF statistics logic 680. Thevarious components of the AF statistics logic 680 are described infurther detail below.

First, the horizontal edge detection process includes applying thehorizontal filter 684 for each color component (R, Gr, Gb, B) followedby an optional edge detector 686 on each color component. Thus,depending on imaging conditions, this configuration allows for the AFstatistic logic 680 to be set up as a high pass filter with no edgedetection (e.g., edge detector disabled) or, alternatively, as a lowpass filter followed by an edge detector (e.g., edge detector enabled).For instance, in low light conditions, the horizontal filter 684 may bemore susceptible to noise and, therefore, the logic 680 may configurethe horizontal filter as a low pass filter followed by an enabled edgedetector 686. As shown, the control signal 694 may enable or disable theedge detector 686. The statistics from the different color channels areused to determine the direction of the focus to improve sharpness, sincethe different colors may focus at different depth. In particular, the AFstatistics logic 680 may provide for techniques to enabling auto-focuscontrol using a combination of coarse and fine adjustments (e.g., to thefocal length of the lens). Embodiments of such techniques are describedin additional detail below.

In one embodiment the horizontal filter may be a 7-tap filter and may bedefined as follows in Equations 41 and 42:

out(i)=(af_horzfilt_coeff [0]*(in(i−3)+in(i+3))+af_horzfilt_coeff[1]*(in(i−2)+in(i+2))+af_horzfilt_coeff[2]*(in(i−1)+in(i+1))+af_horzfilt_coeff [3]*in(i))  (41)

out(i)=max(−255,min(255,out(i)))  (42)

Here, each coefficient af_horzfilt_coeff [0:3] may be in the range [−2,2], and i represents the input pixel index for R, Gr, Gb or B. Thefiltered output out(i) may be clipped between a minimum and maximumvalue of −255 and 255, respectively (Equation 42). The filtercoefficients may be defined independently per color component.

The optional edge detector 686 may follow the output of the horizontalfilter 684. In one embodiment, the edge detector 686 may be defined as:

edge(i)=abs(−2*out(i−1)+2*out(i+1))+abs(−out(i−2)+out(i+2))  (43)

edge(i)=max(0,min(255,edge(i)))  (44)

Thus, the edge detector 686, when enabled, may output a value based uponthe two pixels on each side of the current input pixel i, as depicted byEquation 43. The result may be clipped to an 8-bit value between 0 and255, as shown in Equation 44.

Depending on whether an edge is detected, the final output of the pixelfilter (e.g., filter 684 and detector 686) may be selected as either theoutput of the horizontal filter 684 or the output of the edge detector686. For instance, as shown in Equation 45, the output of the edgedetector 686 may be edge(i) if an edge is detected, or may be theabsolute value of the horizontal filter output out(i) if no edge isdetected.

edge(i)=(af_horzfilt_edge_detected)?edge(i):abs(out(i))  (45)

For each window the accumulated values, edge_sum[R, Gr, Gb, B], may beselected to be either (1) the sum of edge(j,i) for each pixel over thewindow, or (2) the maximum value of edge(i) across a line in the window,max(edge), summed over the lines in the window. Assuming a raw framesize of 4096×4096 pixels, the number of bits required to store themaximum values of edge_sum[R, Gr, Gb, B] is 30 bits (e.g., 8 bits perpixel, plus 22 bits for a window covering the entire raw image frame).

As discussed, the 3×3 filters 690 for camY luma may include twoprogrammable 3×3 filters, referred to as F0 and F1, which are applied tocamY. The result of the filter 690 goes to either a squared function oran absolute value function. The result is accumulated over a given AFwindow for both 3×3 filters F0 and F1 to generate a luma edge value. Inone embodiment, the luma edge values at each camY pixel are defined asfollows:

edgecamY _(—)FX(j,i)=FX*camY=FX(0,0)*camY(j−1,i−1)+FX(0,1)*camY(j−1,i)+FX(0,2)*camY(j−1,i+1)+FX(1,0)*camY(j,i−1)+FX(1,1)*camY(j,i)+FX(1,2)*camY(j,i+1)+FX(2,0)*camY(j+1,i−1)+FX(2,1)*camY(j+1,i)+FX(2,2)*camY(j+1,i+1)  (46)

edgecamY _(—) FX(j,i)=f(max(−255,min(255,edgecamY _(—) FX(j,i))))  (47)

f(a)=â2 or abs(a)

where FX represents the 3×3 programmable filters, F0 and F1, with signedcoefficients in the range [−4, 4]. The indices j and i represent pixellocations in the camY image. As discussed above, the filter on camY mayprovide coarse resolution statistics, since camY is derived usingdown-scaled (e.g., 4×4 to 1) Bayer RGB data. For instance, in oneembodiment, the filters F0 and F1 may be set using a Scharr operator,which offers improved rotational symmetry over a Sobel operator, anexample of which is shown below:

${F\; 0} = \begin{bmatrix}{- 3} & 0 & 3 \\{- 10} & 0 & 10 \\{- 3} & 0 & 3\end{bmatrix}$ ${F\; 1} = \begin{bmatrix}{- 3} & {- 10} & {- 3} \\0 & 0 & 0 \\3 & 10 & 3\end{bmatrix}$

For each window, the accumulated values 700 determined by the filters690, edgecamY_FX sum (where FX=F0 and F1), can selected to be either (1)the sum of edgecamY_FX(j,i) for each pixel over the window, or (2) themaximum value of edgecamY_FX(j) across a line in the window, summed overthe lines in the window. In one embodiment, edgecamY_FX_sum may saturateto a 32-bit value when f(a) is set to â2 to provide “peakier” statisticswith a finer resolution. To avoid saturation, a maximum window size X*Yin raw frame pixels may be set such that it does not exceed a total of1024×1024 pixels (e.g., i.e. X*Y<=1048576 pixels). As noted above, f(a)may also be set as an absolute value to provide more linear statistics.

The AF 3×3 filters 688 on Bayer Y may defined in a similar manner as the3×3 filters in camY, but they are applied to luma values Y generatedfrom a Bayer quad (2×2 pixels). First, 8-bit Bayer RGB values areconverted to Y with programmable coefficients in the range [0, 4] togenerate a white balanced Y value, as shown below in Equation 48:

bayerY=max(0,min(255,bayerY_Coeff [0]*R+bayerY_Coeff[1]*(Gr+Gb)/2+bayerY_Coeff [2]*B))  (48)

Like the filters 690 for camY, the 3×3 filters 688 for bayerY luma mayinclude two programmable 3×3 filters, referred to as F0 and F1, whichare applied to bayerY. The result of the filter 688 goes to either asquared function or an absolute value function. The result isaccumulated over a given AF window for both 3×3 filters F0 and F1 togenerate a luma edge value. In one embodiment, the luma edge values ateach bayerY pixel are defined as follows:

edgebayerY _(—)FX(j,i)=FX*bayerY=FX(0,0)*bayerY(j−1,i−1)+FX(0,1)*bayerY(j−1,i)+FX(0,2)*bayerY(j−1,i)+FX(1,0)*bayerY(j,i−1)+FX(1,1)*bayerY(j,i)+FX(1,2)*bayerY(j−1,i)+FX(2,0)*bayerY(j+1,i−1)+FX(2,1)*bayerY(j+1,i)+FX(2,2)*bayerY(j+1,i)  (49)

edgebayerY _(—) FX(j,i)=f(max(−255,min(255,edgebayerY _(—)FX(j,i))))  (50)

f(a)=â2 or abs(a)

where FX represents the 3×3 programmable filters, F0 and F1, with signedcoefficients in the range [−4, 4]. The indices j and i represent pixellocations in the bayerY image. As discussed above, the filter on Bayer Ymay provide fine resolution statistics, since the Bayer RGB signalreceived by the AF logic 680 is not decimated. By way of examples only,the filters F0 and F1 of the filter logic 688 may be set using one ofthe following filter configurations:

${\begin{bmatrix}{- 1} & {- 1} & {- 1} \\{- 1} & 8 & {- 1} \\{- 1} & {- 1} & {- 1}\end{bmatrix}\begin{bmatrix}{- 6} & 10 & 6 \\10 & 0 & {- 10} \\6 & {- 10} & {- 6}\end{bmatrix}}\begin{bmatrix}0 & {- 1} & 0 \\{- 1} & 2 & 0 \\0 & 0 & 0\end{bmatrix}$

For each window, the accumulated values 702 determined by the filters688, edgebayerY_FX_sum (where FX=F0 and F1), can selected to be either(1) the sum of edgebayerY_FX(j,i) for each pixel over the window, or (2)the maximum value of edgebayerY_FX(j) across a line in the window,summed over the lines in the window. Here, edgebayerY_FX_sum maysaturates to 32-bits when f(a) is set to â2. Thus, to avoid saturation,the maximum window size X*Y in raw frame pixels should be set such thatit does not exceed a total of 512×512 pixels (e.g., X*Y<=262144). Asdiscussed above, setting f(a) to â2 may provide for peakier statistics,while setting f(a) to abs(a) may provide for more linear statistics.

As discussed above, statistics 682 for AF are collected for 16 windows.The windows may be any rectangular area with each dimension being amultiple of 4 pixels. Because each filtering logic 688 and 690 includestwo filters, in some instances, one filter may be used for normalizationover 4 pixels, and may be configured to filter in both vertical andhorizontal directions. Further, in some embodiments, the AF logic 680may normalize the AF statistics by brightness. This may be accomplishedby setting one or more of the filters of the logic blocks 688 and 690 asbypass filters. In certain embodiments, the location of the windows maybe restricted to multiple of 4 pixels, and windows are permitted tooverlap. For instance, one window may be used to acquire normalizationvalues, while another window may be used for additional statistics, suchas variance, as discussed below. In one embodiment, the AF filters(e.g., 684, 688, 690) may not implement pixel replication at the edge ofan image frame and, therefore, in order for the AF filters to use allvalid pixels, the AF windows may be set such that they are each at least4 pixels from the top edge of the frame, at least 8 pixels from thebottom edge of the frame and at least 12 pixels from the left/right edgeof the frame. In the illustrated embodiment, the following statisticsmay be collected and reported for each window:

32-bit edgeGr_sum for Gr

32-bit edgeR_sum for R

32-bit edgeB_sum for B

32-bit edgeGb_sum for Gb

32-bit edgebayerY_F0_sum for Y from Bayer for filter0 (F0)

32-bit edgebayerY_F1_sum for Y from Bayer for filter1 (F1)

32-bit edgecamY_F0_sum for camY for filter0 (F0)

32-bit edgecamY_F1_sum for camY for filter1 (F1)

In such an embodiment, the memory required for storing the AF statistics682 may be 16 (windows) multiplied by 8 (Gr, R, B, Gb, bayerY_F0,bayerY_F1, camY_F0, camY_F1) multiplied by 32 bits.

Thus, in one embodiment, the accumulated value per window may beselected between: the output of the filter (which may be configured as adefault setting), the input pixel, or the input pixel squared. Theselection may be made for each of the 16 AF windows, and may apply toall of the 8 AF statistics (listed above) in a given window. This may beused to normalize the AF score between two overlapping windows, one ofwhich is configured to collect the output of the filter and one of whichis configured to collect the input pixel sum. Additionally, forcalculating pixel variance in the case of two overlapping windows, onewindow may be configured to collect the input pixel sum, and another tocollect the input pixel squared sum, thus providing for a variance thatmay be calculated as:

Variance=(avg_pixel²)−(avg_pixel)̂2

Using the AF statistics, the ISP control logic 84 (FIG. 7) may beconfigured to adjust a focal length of the lens of an image device(e.g., 30) using a series of focal length adjustments based on coarseand fine auto-focus “scores” to bring an image into focus. As discussedabove, the 3×3 filters 690 for camY may provide for coarse statistics,while the horizontal filter 684 and edge detector 686 may provide forcomparatively finer statistics per color component, while the 3×3filters 688 on BayerY may provide for fine statistics on BayerY.Further, the 3×3 filters 706 on a decimated Bayer RGB signal 705 mayprovide coarse statistics for each color channel. As discussed furtherbelow, AF scores may be calculated based on filter output values for aparticular input signal (e.g., sum of filter outputs F0 and F1 for camY,BayerY, Bayer RGB decimated, or based on horizontal/edge detectoroutputs, etc.).

FIG. 61 shows a graph 710 that depicts curves 712 and 714 whichrepresent coarse and fine AF scores, respectively. As shown, the coarseAF scores based upon the coarse statistics may have a more linearresponse across the focal distance of the lens. Thus, at any focalposition, a lens movement may generate a change in an auto focus scorewhich may be used to detect if the image is becoming more in focus orout of focus. For instance, an increase in a coarse AF score after alens adjustment may indicate that the focal length is being adjusted inthe correct direction (e.g., towards the optical focal position).

However, as the optical focal position is approached, the change in thecoarse AF score for smaller lens adjustments steps may decrease, makingit difficult to discern the correct direction of focal adjustment. Forexample, as shown on graph 710, the change in coarse AF score betweencoarse position (CP) CP1 and CP2 is represented by Δ_(C12), which showsan increase in the coarse from CP1 to CP2. However, as shown, from CP3to CP4, the change Δ_(C34) in the coarse AF score (which passes throughthe optimal focal position (OFP)), though still increasing, isrelatively smaller. It should be understood that the positions CP1-CP6along the focal length L are not meant to necessarily correspond to thestep sizes taken by the auto-focus logic along the focal length. Thatis, there may be additional steps taken between each coarse positionthat are not shown. The illustrated positions CP1-CP6 are only meant toshow how the change in the coarse AF score may gradually decrease as thefocal position approaches the OFP.

Once the approximate position of the OFP is determined (e.g., based onthe coarse AF scores shown in FIG. 61, the approximate position of theOFP may be between CP3 and CP5), fine AF score values, represented bycurve 714 may be evaluated to refine the focal position. For instance,fine AF scores may be flatter when the image is out of focus, so that alarge lens positional change does not cause a large change in the fineAF score. However, as the focal position approaches the optical focalposition (OFP), the fine AF score may change sharply with smallpositional adjustments. Thus, by locating a peak or apex 715 on the fineAF score curve 714, the OFP may be determined for the current imagescene. Thus, to summarize, coarse AF scores may be used to determine thegeneral vicinity of the optical focal position, while the fine AF scoresmay be used to pinpoints a more exact position within that vicinity.

In one embodiment, the auto-focus process may begin by acquiring coarseAF scores along the entire available focal length, beginning at position0 and ending at position L (shown on graph 710) and determine the coarseAF scores at various step positions (e.g., CP1-CP6). In one embodiment,once the focal position of the lens has reached position L, the positionmay reset to 0 before evaluating AF scores at various focal positions.For instance, this may be due to coil settling time of a mechanicalelement controlling the focal position. In this embodiment, afterresetting to position 0, the focal position may be adjusted towardposition L to a position that first indicated a negative change in acoarse AF score, here position CP5 exhibiting a negative change Δ_(C45)with respect to position CP4. From position CP5, the focal position maybe adjusted in smaller increments relative to increments used in thecoarse AF score adjustments (e.g., positions FP1, FP2, FP3, etc.) backin the direction towards position 0, while searching for a peak 715 inthe fine AF score curve 714. As discussed above, the focal position OFPcorresponding to the peak 715 in the fine AF score curve 714 may be theoptimal focal position for the current image scene.

As will be appreciated, the techniques described above for locating theoptimal area and optimal position for focus may be referred to as “hillclimbing,” in the sense that the changes in the curves for the AF scores712 and 714 are analyzed to locate the OFP. Further, while the analysisof the coarse AF scores (curve 712) and the fine AF scores (curve 714)is shown as using same-sized steps for coarse score analysis (e.g.,distance between CP1 and CP2) and same-sized steps for fine scoreanalysis (e.g., distance between FP1 and FP2), in some embodiments, thestep sizes may be varied depending on the change in the score from oneposition to the next. For instance, in one embodiment, the step sizebetween CP3 and CP4 may be reduced relative to the step size between CP1and CP2 since the overall delta in the coarse AF score (Δ_(C34)) is lessthen the delta from CP1 to CP2 (Δ_(C12)).

A method 720 depicting this process is illustrated in FIG. 62. Beginningat block 722, a coarse AF score is determined for image data at varioussteps along the focal length, from position 0 to position L (FIG. 61).Thereafter, at block 724, the coarse AF scores are analyzed and thecoarse position exhibiting the first negative change in the coarse AFscore is identified as a starting point for fine AF scoring analysis.For instance, subsequently, at block 726, the focal position is steppedback towards the initial position 0 at smaller steps, with the fine AFscore at each step being analyzed until a peak in the AF score curve(e.g., curve 714 of FIG. 61) is located. At block 728, the focalposition corresponding to the peak is set as the optimal focal positionfor the current image scene.

As discussed above, due to mechanical coil settling times, theembodiment of the technique shown in FIG. 62 may be adapted to acquirecoarse AF scores along the entire focal length initially, rather thananalyzing each coarse position one by one and searching for an optimalfocus area. Other embodiments, however, in which coil settling times areless of a concern, may analyze coarse AF scores one by one at each step,instead of searching the entire focal length.

In certain embodiments, the AF scores may be determined using whitebalanced luma values derived from Bayer RGB data. For instance, the lumavalue, Y, may be derived by decimating a 2×2 Bayer quad by a factor of2, as shown in FIG. 63, or by decimating a 4×4 pixel block consisting offour 2×2 Bayer quads by a factor of 4, as shown in FIG. 64. In oneembodiment, AF scores may be determined using gradients. In anotherembodiment, AF scores may be determined by applying a 3×3 transformusing a Scharr operator, which provides rotational symmetry whileminimizing weighted mean squared angular errors in the Fourier domain.By way of example, the calculation of a coarse AF score on camY using acommon Scharr operator (discussed above) is shown below:

${{AFScore}_{coarse} = {{f\left( {\begin{bmatrix}{- 3} & 0 & 3 \\{- 10} & 0 & 10 \\{- 3} & 0 & 3\end{bmatrix} \times {in}} \right)} + {f\left( {\begin{bmatrix}{- 3} & {- 10} & {- 3} \\0 & 0 & 0 \\3 & 10 & 3\end{bmatrix} \times {in}} \right)}}},$

where in represents the decimated luma Y value. In other embodiments,the AF score for both coarse and fine statistics may be calculated usingother 3×3 transforms.

Auto focus adjustments may also be performed differently depending onthe color components, since different wavelengths of light may beaffected differently by the lens, which is one reason the horizontalfilter 684 is applied to each color component independently. Thus,auto-focus may still be performed even in the present of chromaticaberration in the lens. For instance, because red and blue typicallyfocuses at a different position or distance with respect to green whenchromatic aberrations are present, relative AF scores for each color maybe used to determine the direction to focus. This is better illustratedin FIG. 65, which shows the optimal focal position for blue, red, andgreen color channels for a lens 740. As shown, the optimal focalpositions for red, green, and blue are depicted by reference letters R,G, and B respectively, each corresponding to an AF score, with a currentfocal position 742. Generally, in such a configuration, it may bedesirable to select the optimal focus position as the positioncorresponding to the optimal focal position for green components (e.g.,since Bayer RGB has twice as many green as red or blue components), hereposition G. Thus, it may be expected that for an optimal focal position,the green channel should exhibit the highest auto-focus score. Thus,based on the positions of the optimal focal positions for each color(with those closer to the lens having higher AF scores), the AF logic680 and associated control logic 84 may determine which direction tofocus based on the relative AF scores for blue, green, and red. Forinstance, if the blue channel has a higher AF score relative to thegreen channel (as shown in FIG. 65), then the focal position is adjustedin the negative direction (towards the image sensor) without having tofirst analyze in the positive direction from the current position 742.In some embodiments, illuminant detection or analysis using colorcorrelated temperatures (CCT) may be performed.

Further, as mentioned above, variance scores may also be used. Forinstance, pixel sums and pixel squared sum values may be accumulated forblock sizes (e.g., 8×8-32×32 pixels), and may be used to derive variancescores (e.g., avg_pixel²)-(avg_pixel)̂2). The variances may be summed toget a total variance score for each window. Smaller block sizes may beused to obtain fine variance scores, and larger block sizes may be usedto obtain coarser variance scores.

Referring to the 3A statistics logic 468 of FIG. 51, the logic 468 mayalso be configured to collect component histograms 750 and 752. As willbe appreciated, histograms may be used to analyze the pixel leveldistribution in an image. This may be useful for implementing certainfunctions, such as histogram equalization, where the histogram data isused to determine the histogram specification (histogram matching). Byway of example, luma histograms may be used for AE (e.g., foradjusting/setting sensor integration times), and color histograms may beused for AWB. In the present embodiment, histograms may be 256, 128, 64or 32 bins (where the top 8, 7, 6, and 5 bits of the pixel is used todetermine the bin, respectively) for each color component, as specifiedby a bin size (BinSize). For instance, when pixel data is 14-bits, anadditional scale factor between 0-6 and an offset may be specified todetermine what range (e.g., which 8 bits) of the pixel data is collectedfor statistics purposes. The bin number may be obtained as follows:

idx=((pixel−hist_offset)>>(6−hist_scale)

In one embodiment, the color histogram bins are incremented only if thebin indices are in the range [0, 2̂(8−BinSize)]:

if (idx>=0 && idx<2̂(8−BinSize))

-   -   StatsHist[idx]+=Count;

In the present embodiment, the statistics processing unit 120 mayinclude two histogram units. This first histogram 750 (Hist0) may beconfigured to collect pixel data as part of the statistics collectionafter the 4×4 decimation. For Hist0, the components may be selected tobe RGB, sRGB_(linear), sRGB or YC1C2 using selection circuit 756. Thesecond histogram 752 (Hist1) may be configured to collect pixel databefore the statistics pipeline (before defective pixel correction logic460), as shown in more detail in FIG. 65. For instance, the raw BayerRGB data (output from 124) may be decimated (to produce signal 754)using logic 760 by skipping pixels, as discussed further below. For thegreen channel, the color may be selected between Gr, Gb or both Gr andGb (both Gr and Gb counts are accumulated in the Green bins).

In order to keep the histogram bin width the same between the twohistograms, Histl may be configured to collect pixel data every 4 pixels(every other Bayer quad). The start of the histogram window determinesthe first Bayer quad location where the histogram starts accumulating.Starting at this location, every other Bayer quad is skippedhorizontally and vertically for Hist1. The window start location can beany pixel position for Hist1 and, therefore pixels being skipped by thehistogram calculation can be selected by changing the start windowlocation. Hist1 can be used to collect data, represented by 1112 in FIG.66, close to the black level to assist in dynamic black levelcompensation at block 462. Thus, while shown in FIG. 66 as beingseparate from the 3A statistics logic 468 for illustrative purposes, itshould be understood that the histogram 752 may actually be part of thestatistics written to memory, and may be actually be physically locatedwithin the statistics processing unit 120.

In the present embodiment, the red (R) and blue (B) bins may be 20-bits,with the green (G) bin is 21-bits (Green is larger to accommodate the Grand Gb accumulation in Hist1). This allows for a maximum picture size of4160 by 3120 pixels (12 MP). The internal memory size required is3×256×20(1) bits (3 color components, 256 bins).

With regard to memory format, statistics for AWB/AE windows, AF windows,2D color histogram, and component histograms may be mapped to registersto allow early access by firmware. In one embodiment, two memorypointers may be used to write statistics to memory, one for tilestatistics 674, and one for luma row sums 678, followed by all othercollected statistics. All statistics are written to external memory,which may be DMA memory. The memory address registers may bedouble-buffered so that a new location in memory can be specified onevery frame.

Before proceeding with a detailed discussion of the ISP pipe logic 82downstream from the ISP front-end logic 80, it should understood thatthe arrangement of various functional logic blocks in the statisticsprocessing units 120 and 122 (e.g., logic blocks 460, 462, 464, 466, and468) and the ISP front-end pixel processing unit 130 (e.g., logic blocks298 and 300) are intended to illustrate only one embodiment of thepresent technique. Indeed, in other embodiments, the logic blocksillustrated herein may be arranged in different ordering, or may includeadditional logic blocks that may perform additional image processingfunctions not specifically described herein. Further, it should beunderstood that the image processing operations performed in thestatistics processing units (e.g., 120 and 122), such as lens shadingcorrection, defective pixel detection/correction, and black levelcompensation, are performed within the statistics processing units forthe purposes of collecting statistical data. Thus, processing operationsperformed upon the image data received by the statistical processingunits are not actually reflected in the image signal 109 (FEProcOut)that is output from the ISP front-end pixel processing logic 130 andforwarded to the ISP pipe processing logic 82.

Before continuing, it should also be noted, that given sufficientprocessing time and the similarity between many of the processingrequirements of the various operations described herein, it is possibleto reconfigure the functional blocks shown herein to perform imageprocessing in a sequential manner, rather than a pipe-lined nature. Aswill be understood, this may further reduce the overall hardwareimplementation costs, but may also increase bandwidth to external memory(e.g., to cache/store intermediate results/data).

The ISP Pipeline (“Pipe”) Processing Logic

Having described the ISP front-end logic 80 in detail above, the presentdiscussion will now shift focus to the ISP pipe processing logic 82.Generally, the function of the ISP pipe logic 82 is to receive raw imagedata, which may be provided from the ISP front-end logic 80 or retrievedfrom memory 108, and to perform additional image processing operations,i.e., prior to outputting the image data to the display device 28.

A block diagram showing an embodiment of the ISP pipe logic 82 isdepicted in FIG. 67. As illustrated, the ISP pipe logic 82 may includeraw processing logic 900, RGB processing logic 902, and YCbCr processinglogic 904. The raw processing logic 900 may perform various imageprocessing operations, such as defective pixel detection and correction,lens shading correction, demosaicing, as well as applying gains forauto-white balance and/or setting a black level, as will be discussedfurther below. As shown in the present embodiment, the input signal 908to the raw processing logic 900 may be the raw pixel output 109 (signalFEProcOut) from the ISP front-end logic 80 or the raw pixel data 112from the memory 108, depending on the present configuration of theselection logic 906.

As a result of demosaicing operations performed within the rawprocessing logic 900, the image signal output 910 may be in the RGBdomain, and may be subsequently forwarded to the RGB processing logic902. For instance, as shown in FIG. 67, the RGB processing logic 902receives the signal 916, which may be the output signal 910 or an RGBimage signal 912 from the memory 108, depending on the presentconfiguration of the selection logic 914. The RGB processing logic 902may provide for various RGB color adjustment operations, including colorcorrection (e.g., using a color correction matrix), the application ofcolor gains for auto-white balancing, as well as global tone mapping, aswill be discussed further below. The RGB processing logic 904 may alsoprovide for the color space conversion of RGB image data to the YCbCr(luma/chroma) color space. Thus, the image signal output 918 may be inthe YCbCr domain, and may be subsequently forwarded to the YCbCrprocessing logic 904.

For instance, as shown in FIG. 67, the YCbCr processing logic 904receives the signal 924, which may be the output signal 918 from the RGBprocessing logic 902 or a YCbCr signal 920 from the memory 108,depending on the present configuration of the selection logic 922. Aswill be discussed in further detail below, the YCbCr processing logic904 may provide for image processing operations in the YCbCr colorspace, including scaling, chroma suppression, luma sharpening,brightness, contrast, and color (BCC) adjustments, YCbCr gamma mapping,chroma decimation, and so forth. The image signal output 926 of theYCbCr processing logic 904 may be sent to the memory 108, or may beoutput from the ISP pipe processing logic 82 as the image signal 114(FIG. 7). The image signal 114 may be sent to the display device 28(either directly or via memory 108) for viewing by the user, or may befurther processed using a compression engine (e.g., encoder 118), aCPU/GPU, a graphics engine, or the like.

In accordance with embodiments of the present techniques, the ISP pipelogic 82 may support the processing of raw pixel data in 8-bit, 10-bit,12-bit, or 14-bit formats. For instance, in one embodiment, 8-bit,10-bit, or 12-bit input data may be converted to 14-bit at the input ofthe raw processing logic 900, and raw processing and RGB processingoperations may be performed with 14-bit precision. In the latterembodiment, the 14-bit image data may be down-sampled to 10 bits priorto the conversion of the RGB data to the YCbCr color space, and theYCbCr processing (logic 904) may be performed with 10-bit precision.

In order to provide a comprehensive description of the various functionsprovided by the ISP pipe processing logic 82, each of the raw processinglogic 900, RGB processing logic 902, and YCbCr processing logic 904, aswell as internal logic for performing various image processingoperations that may be implemented in each respective unit of logic 900,902, and 904, will be discussed sequentially below, beginning with theraw processing logic 900. For instance, referring now to FIG. 68, ablock diagram showing a more detailed view of an embodiment of the rawprocessing logic 900 is illustrated, in accordance with an embodiment ofthe present technique. As shown, the raw processing logic 900 includesthe gain, offset, and clamping (GOC) logic 930, defective pixeldetection/correction (DPDC) logic 932, the noise reduction logic 934,lens shading correction logic 936, GOC logic 938, and demosaicing logic940. Further, while the examples discussed below assume the use of aBayer color filter array with the image sensor(s) 90, it should beunderstood that other embodiments of the present technique may utilizedifferent types of color filters as well.

The input signal 908, which may be a raw image signal, is first receivedby the gain, offset, and clamping (GOC) logic 930. The GOC logic 930 mayprovide similar functions and may be implemented in a similar mannerwith respect to the BLC logic 462 of the statistics processing unit 120of the ISP front-end logic 80, as discussed above in FIG. 37. Forinstance, the GOC logic 930 may provide digital gain, offsets andclamping (clipping) independently for each color component R, B, Gr, andGb of a Bayer image sensor. Particularly, the GOC logic 930 may performauto-white balance or set the black level of the raw image data.Further, in some embodiments, the GOC logic 930 may also be used corrector compensate for an offset between the Gr and Gb color components.

In operation, the input value for the current pixel is first offset by asigned value and multiplied by a gain. This operation may be performedusing the formula shown in Equation 11 above, wherein X represents theinput pixel value for a given color component R, B, Gr, or Gb, O[c]represents a signed 16-bit offset for the current color component c, andG[c] represents a gain value for the color component c. The values forG[c] may be previously determined during statistics processing (e.g., inthe ISP front-end block 80). In one embodiment, the gain G[c] may be a16-bit unsigned number with 2 integer bits and 14 fraction bits (e.g.,2.14 floating point representation), and the gain G[c] may be appliedwith rounding. By way of example only, the gain G[c] may have a range ofbetween 0 to 4×.

The computed pixel value Y (which includes the gain G[c] and offsetO[c]) from Equation 11 is then be clipped to a minimum and a maximumrange in accordance with Equation 12. As discussed above, the variablesmin[c] and max[c] may represent signed 16-bit “clipping values” for theminimum and maximum output values, respectively. In one embodiment, theGOC logic 930 may also be configured to maintain a count of the numberof pixels that were clipped above and below maximum and minimum ranges,respectively, for each color component.

Subsequently, the output of the GOC logic 930 is forwarded to thedefective pixel detection and correction logic 932. As discussed abovewith reference to FIG. 37 (DPDC logic 460), defective pixels mayattributable to a number of factors, and may include “hot” (or leaky)pixels, “stuck” pixels, and “dead pixels, wherein hot pixels exhibit ahigher than normal charge leakage relative to non-defective pixels, andthus may appear brighter than non-defective pixel, and wherein a stuckpixel appears as always being on (e.g., fully charged) and thus appearsbrighter, whereas a dead pixel appears as always being off As such, itmay be desirable to have a pixel detection scheme that is robust enoughto identify and address different types of failure scenarios.Particularly, when compared to the front-end DPDC logic 460, which mayprovide only dynamic defect detection/correction, the pipe DPDC logic932 may provide for fixed or static defect detection/correction, dynamicdefect detection/correction, as well as speckle removal.

In accordance with embodiments of the presently disclosed techniques,defective pixel correction/detection performed by the DPDC logic 932 mayoccur independently for each color component (e.g., R, B, Gr, and Gb),and may include various operations for detecting defective pixels, aswell as for correcting the detected defective pixels. For instance, inone embodiment, the defective pixel detection operations may provide forthe detection of static defects, dynamics defects, as well as thedetection of speckle, which may refer to the electrical interferences ornoise (e.g., photon noise) that may be present in the imaging sensor. Byanalogy, speckle may appear on an image as seemingly random noiseartifacts, similar to the manner in which static may appear on adisplay, such as a television display. Further, as noted above, dynamicdefection correction is regarded as being dynamic in the sense that thecharacterization of a pixel as being defective at a given time maydepend on the image data in the neighboring pixels. For example, a stuckpixel that is always on maximum brightness may not be regarded as adefective pixel if the location of the stuck pixel is in an area of thecurrent image that is dominate by bright white colors. Conversely, ifthe stuck pixel is in a region of the current image that is dominated byblack or darker colors, then the stuck pixel may be identified as adefective pixel during processing by the DPDC logic 932 and correctedaccordingly.

With regard to static defect detection, the location of each pixel iscompared to a static defect table, which may store data corresponding tothe location of pixels that are known to be defective. For instance, inone embodiment, the DPDC logic 932 may monitor the detection ofdefective pixels (e.g., using a counter mechanism or register) and, if aparticular pixel is observed as repeatedly failing, the location of thatpixel is stored into the static defect table. Thus, during static defectdetection, if it is determined that the location of the current pixel isin the static defect table, then the current pixel is identified asbeing a defective pixel, and a replacement value is determined andtemporarily stored. In one embodiment, the replacement value may be thevalue of the previous pixel (based on scan order) of the same colorcomponent. The replacement value may be used to correct the staticdefect during dynamic/speckle defect detection and correction, as willbe discussed below. Additionally, if the previous pixel is outside ofthe raw frame 278 (FIG. 19), then its value is not used, and the staticdefect may be corrected during the dynamic defect correction process.Further, due to memory considerations, the static defect table may storea finite number of location entries. For instance, in one embodiment,the static defect table may be implemented as a FIFO queue configured tostore a total of 16 locations for every two lines of image data. Thelocations in defined in the static defect table will, nonetheless, becorrected using a previous pixel replacement value (rather than via thedynamic defect detection process discussed below). As mentioned above,embodiments of the present technique may also provide for updating thestatic defect table intermittently over time.

Embodiments may provide for the static defect table to be implemented inon-chip memory or off-chip memory. As will be appreciated, using anon-chip implementation may increase overall chip area/size, while usingan off-chip implementation may reduce chip area/size, but increasememory bandwidth requirements. Thus, it should be understood that thestatic defect table may be implemented either on-chip or off-chipdepending on specific implementation requirements, i.e., the totalnumber of pixels that are to be stored within the static defect table.

The dynamic defect and speckle detection processes may be time-shiftedwith respect to the static defect detection process discussed above. Forinstance, in one embodiment, the dynamic defect and speckle detectionprocess may begin after the static defect detection process has analyzedtwo scan lines (e.g., rows) of pixels. As can be appreciated, thisallows for the identification of static defects and their respectivereplacement values to be determined before dynamic/speckle detectionoccurs. For example, during the dynamic/speckle detection process, ifthe current pixel was previously marked as being a static defect, ratherthan applying dynamic/speckle detection operations, the static defect issimply corrected using the previously assessed replacement value.

With regard to dynamic defect and speckle detection, these processes mayoccur sequentially or in parallel. The dynamic defect and speckledetection and correction that is performed by the DPDC logic 932 mayrely on adaptive edge detection using pixel-to-pixel directiongradients. In one embodiment, the DPDC logic 932 may select the eightimmediate neighbors of the current pixel having the same color componentthat are within the raw frame 278 (FIG. 19) are used. In other words,the current pixels and its eight immediate neighbors P0, P1, P2, P3, P4,P5, P6, and P7 may form a 3×3 area, as shown below in FIG. 69.

It should be noted, however, that depending on the location of thecurrent pixel P, pixels outside the raw frame 278 are not consideredwhen calculating pixel-to-pixel gradients. For example, with regard tothe “top-left” case 942 shown in FIG. 69, the current pixel P is at thetop-left corner of the raw frame 278 and, thus, the neighboring pixelsP0, P1, P2, P3, and P5 outside of the raw frame 278 are not considered,leaving only the pixels P4, P6, and P7 (N=3). In the “top” case 944, thecurrent pixel P is at the top-most edge of the raw frame 278 and, thus,the neighboring pixels P0, P1, and P2 outside of the raw frame 278 arenot considered, leaving only the pixels P3, P4, P5, P6, and P7 (N=5).Next, in the “top-right” case 946, the current pixel P is at thetop-right corner of the raw frame 278 and, thus, the neighboring pixelsP0, P1, P2, P4, and P7 outside of the raw frame 278 are not considered,leaving only the pixels P3, P5, and P6 (N=3). In the “left” case 948,the current pixel P is at the left-most edge of the raw frame 278 and,thus, the neighboring pixels P0, P3, and P5 outside of the raw frame 278are not considered, leaving only the pixels P1, P2, P4, P6, and P7(N=5).

In the “center” case 950, all pixels P0-P7 lie within the raw frame 278and are thus used in determining the pixel-to-pixel gradients (N=8). Inthe “right” case 952, the current pixel P is at the right-most edge ofthe raw frame 278 and, thus, the neighboring pixels P2, P4, and P7outside of the raw frame 278 are not considered, leaving only the pixelsP0, P1, P3, P5, and P6 (N=5). Additionally, in the “bottom-left” case954, the current pixel P is at the bottom-left corner of the raw frame278 and, thus, the neighboring pixels P0, P3, P5, P6, and P7 outside ofthe raw frame 278 are not considered, leaving only the pixels P1, P2,and P4 (N=3). In the “bottom” case 956, the current pixel P is at thebottom-most edge of the raw frame 278 and, thus, the neighboring pixelsP5, P6, and P7 outside of the raw frame 278 are not considered, leavingonly the pixels P0, P1, P2, P3, and P4 (N=5). Finally, in the“bottom-right” case 958, the current pixel P is at the bottom-rightcorner of the raw frame 278 and, thus, the neighboring pixels P2, P4,P5, P6, and P7 outside of the raw frame 278 are not considered, leavingonly the pixels P0, P1, and P3 (N=3).

Thus, depending upon the position of the current pixel P, the number ofpixels used in determining the pixel-to-pixel gradients may be 3, 5, or8. In the illustrated embodiment, for each neighboring pixel (k=0 to 7)within the picture boundary (e.g., raw frame 278), the pixel-to-pixelgradients may be calculated as follows:

G _(k)=abs(P−P _(k)), for 0≦k≦7 (only for k within the raw frame)  (51)

Additionally, an average gradient, G_(av), may be calculated as thedifference between the current pixel and the average, P_(av), of itssurrounding pixels, as shown by the equations below:

$\begin{matrix}{{{P_{av} = \frac{\left( {\sum\limits_{k}^{N}P_{k}} \right)}{N}},{{{wherein}\mspace{14mu} N} = 3},5,{or}}\mspace{14mu} 8\left( {{depending}\mspace{14mu} {on}\mspace{14mu} {pixel}\mspace{14mu} {position}} \right)} & \left( {52a} \right) \\{G_{av} = {{abs}\left( {P - P_{av}} \right)}} & \left( {52b} \right)\end{matrix}$

The pixel-to-pixel gradient values (Equation 51) may be used indetermining a dynamic defect case, and the average of the neighboringpixels (Equations 52a and 52b) may be used in identifying speckle cases,as discussed further below.

In one embodiment, dynamic defect detection may be performed by the DPDClogic 932 as follows. First, it is assumed that a pixel is defective ifa certain number of the gradients G_(k) are at or below a particularthreshold, denoted by the variable dynTh (dynamic defect threshold).Thus, for each pixel, a count (C) of the number of gradients forneighboring pixels inside the picture boundaries that are at or belowthe threshold dynTh is accumulated. The threshold dynTh may be acombination of a fixed threshold component and a dynamic thresholdcomponent that may depend on the “activity” present the surroundingpixels. For instance, in one embodiment, the dynamic threshold componentfor dynTh may be determined by calculating a high frequency componentvalue P_(hf) based upon summing the absolute difference between theaverage pixel values P_(av) (Equation 52a) and each neighboring pixel,as illustrated below:

$\begin{matrix}{{P_{hf} = {{\frac{8}{N}{\sum\limits_{k}^{N}{{{abs}\left( {P_{av} - P_{k}} \right)}{\mspace{11mu} \mspace{11mu}}{wherein}\mspace{14mu} N}}} = 3}},5,{{or}\mspace{14mu} 8}} & \left( {52c} \right)\end{matrix}$

In instances where the pixel is located at an image corner (N=3) or atan image edge (N=5), the P_(hf) may be multiplied by the 8/3 or 8/5,respectively. As can be appreciated, this ensures that the highfrequency component P_(hf) is normalized based on eight neighboringpixels (N=8).

Once P_(hf) is determined, the dynamic defect detection threshold dynThmay be computed as shown below:

dynTh=dynTh ₁+(dynTh ₂ ×P _(hf)),  (53)

wherein dynTh₁ represents the fixed threshold component, and whereindynTh₂ represents the dynamic threshold component, and is a multiplierfor P_(hf) in Equation 53. A different fixed threshold component dynTh₁may be provided for each color component, but for each pixel of the samecolor, dynTh₁ is the same. By way of example only, dynTh₁ may be set sothat it is at least above the variance of noise in the image.

The dynamic threshold component dynTh₂ may be determined based on somecharacteristic of the image. For instance, in one embodiment, dynTh₂ maybe determined using stored empirical data regarding exposure and/orsensor integration time. The empirical data may be determined duringcalibration of the image sensor (e.g., 90), and may associate dynamicthreshold component values that may be selected for dynTh₂ with each ofa number of data points. Thus, based upon the current exposure and/orsensor integration time value, which may be determined during statisticsprocessing in the ISP front-end logic 80, dynTh₂ may be determined byselecting the dynamic threshold component value from the storedempirical data that corresponds to the current exposure and/or sensorintegration time value. Additionally, if the current exposure and/orsensor integration time value does not correspond directly to one of theempirical data points, then dynTh₂ may be determined by interpolatingthe dynamic threshold component values associated with the data pointsbetween which the current exposure and/or sensor integration time valuefalls. Further, like the fixed threshold component dynTh₁, the dynamicthreshold component dynTh₂ may have different values for each colorcomponent. Thus, composite threshold value dynTh may vary for each colorcomponent (e.g., R, B, Gr, Gb).

As mentioned above, for each pixel, a count C of the number of gradientsfor neighboring pixels inside the picture boundaries that are at orbelow the threshold dynTh is determined. For instance, for eachneighboring pixel within the raw frame 278, the accumulated count C ofthe gradients G_(k) that are at or below the threshold dynTh may becomputed as follows:

$\begin{matrix}{{{C = {\sum\limits_{k}^{N}\left( {G_{k} \leq {{dyn}\; {Th}}} \right)}},{for}}{0 \leq k \leq {7\left( {{only}\mspace{14mu} {for}\mspace{14mu} k\mspace{14mu} {within}\mspace{14mu} {the}{\mspace{11mu} \;}{raw}\mspace{14mu} {frame}} \right)}}} & (54)\end{matrix}$

Next, if the accumulated count C is determined to be less than or equalto a maximum count, denoted by the variable dynMaxC, then the pixel maybe considered as a dynamic defect. In one embodiment, different valuesfor dynMaxC may be provided for N=3 (corner), N=5 (edge), and N=8conditions. This logic is expressed below:

if (C≦dynMaxC), then the current pixel P is defective.  (55)

As mentioned above, the location of defective pixels may be stored intothe static defect table. In some embodiments, the minimum gradient value(min(G_(k))) calculated during dynamic defect detection for the currentpixel may be stored and may be used to sort the defective pixels, suchthat a greater minimum gradient value indicates a greater “severity” ofa defect and should be corrected during pixel correction before lesssevere defects are corrected. In one embodiment, a pixel may need to beprocessed over multiple imaging frames before being stored into thestatic defect table, such as by filtering the locations of defectivepixels over time. In the latter embodiment, the location of thedefective pixel may be stored into the static defect table only if thedefect appears in a particular number of consecutive images at the samelocation. Further, in some embodiments, the static defect table may beconfigured to sort the stored defective pixel locations based upon theminimum gradient values. For instance, the highest minimum gradientvalue may indicate a defect of greater “severity.” By ordering thelocations in this manner, the priority of static defect correction maybe set, such that the most severe or important defects are correctedfirst. Additionally, the static defect table may be updated over time toinclude newly detected static defects, and ordering them accordinglybased on their respective minimum gradient values.

Speckle detection, which may occur in parallel with the dynamic defectdetection process described above, may be performed by determining ifthe value G_(av) (Equation 52b) is above a speckle detection thresholdspkTh. Like the dynamic defect threshold dynTh, the speckle thresholdspkTh may also include fixed and dynamic components, referred to byspkTh₁ and spkTh₂, respectively. In general, the fixed and dynamiccomponents spkTh₁ and spkTh₂ may be set more “aggressively” compared tothe dynTh₁ and dynTh₂ values, in order to avoid falsely detectingspeckle in areas of the image that may be more heavily textured andothers, such as text, foliage, certain fabric patterns, etc.Accordingly, in one embodiment, the dynamic speckle threshold componentspkTh₂ may be increased for high-texture areas of the image, anddecreased for “flatter” or more uniform areas. The speckle detectionthreshold spkTh may be computed as shown below:

spkTh=spkTh ₁+(spkTh ₂ ×P _(hf)),  (56)

wherein spkTh₁ represents the fixed threshold component, and whereinspkTh₂ represents the dynamic threshold component. The detection ofspeckle may then be determined in accordance with the followingexpression:

if (G _(av)>spkTh), then the current pixel P is speckled.  (57)

Once defective pixels have been identified, the DPDC logic 932 may applypixel correction operations depending on the type of defect detected.For instance, if the defective pixel was identified as a static defect,the pixel is replaced with the stored replacement value, as discussedabove (e.g., the value of the previous pixel of the same colorcomponent). If the pixel was identified as either a dynamic defect or asspeckle, then pixel correction may be performed as follows. First,gradients are computed as the sum of the absolute difference between thecenter pixel and a first and second neighbor pixels (e.g., computationof G_(k) of Equation 51) for four directions, a horizontal (h)direction, a vertical (v) direction, a diagonal-positive direction (dp),and a diagonal-negative direction (dn), as shown below:

G _(h) =G ₃ +G ₄  (58)

G _(v) =G ₁ +G ₆  (59)

G _(dp) =G ₂ +G ₅  (60)

G _(dn) =G ₀ +G ₇  (61)

Next, the corrective pixel value P_(C) may be determined via linearinterpolation of the two neighboring pixels associated with thedirectional gradient G_(h), G_(v), G_(dp), and G_(dn) that has thesmallest value. For instance, in one embodiment, the logic statementbelow may express the calculation of P_(C):

$\begin{matrix}{{{{{{if}\left( {\min==G_{h}} \right)}\mspace{14mu} P_{C}} = \frac{P_{3} + P_{4}}{2}};}{{{{else}\mspace{14mu} {{if}\left( {\min==G_{v}} \right)}\mspace{14mu} P_{C}} = \frac{P_{1} + P_{6}}{2}};}{{{{else}\mspace{14mu} {{if}\left( {\min==G_{dp}} \right)}\mspace{14mu} P_{C}} = \frac{P_{2} + P_{5}}{2}};}{{{{else}\mspace{14mu} {{if}\left( {\min==G_{dn}} \right)}\mspace{14mu} P_{C}} = \frac{P_{0} + P_{7}}{2}};}} & (62)\end{matrix}$

The pixel correction techniques implemented by the DPDC logic 932 mayalso provide for exceptions at boundary conditions. For instance, if oneof the two neighboring pixels associated with the selected interpolationdirection is outside of the raw frame, then the value of the neighborpixel that is within the raw frame is substituted instead. Thus, usingthis technique, the corrective pixel value will be equivalent to thevalue of the neighbor pixel within the raw frame.

It should be noted that the defective pixel detection/correctiontechniques applied by the DPDC logic 932 during the ISP pipe processingis more robust compared to the DPDC logic 460 in the ISP front-end logic80. As discussed in the embodiment above, the DPDC logic 460 performsonly dynamic defect detection and correction using neighboring pixels inonly the horizontal direction, whereas the DPDC logic 932 provides forthe detection and correction of static defects, dynamic defects, as wellas speckle, using neighboring pixels in both horizontal and verticaldirections.

As will be appreciated, the storage of the location of the defectivepixels using a static defect table may provide for temporal filtering ofdefective pixels with lower memory requirements. For instance, comparedto many conventional techniques which store entire images and applytemporal filtering to identify static defects over time, embodiments ofthe present technique only store the locations of defective pixels,which may typically be done using only a fraction of the memory requiredto store an entire image frame. Further, as discussed above, the storingof a minimum gradient value (min(G_(k))), allows for an efficient use ofthe static defect table prioritizing the order of the locations at whichdefective pixels are corrected (e.g., beginning with those that will bemost visible).

Additionally, the use of thresholds that include a dynamic component(e.g., dynTh₂ and spkTh₂) may help to reduce false defect detections, aproblem often encountered in conventional image processing systems whenprocessing high texture areas of an image (e.g., text, foliage, certainfabric patterns, etc.). Further, the use of directional gradients (e.g.,h, v, dp, dn) for pixel correction may reduce the appearance of visualartifacts if a false defect detection occurs. For instance, filtering inthe minimum gradient direction may result in a correction that stillyields acceptable results under most cases, even in cases of falsedetection. Additionally, the inclusion of the current pixel P in thegradient calculation may improve the accuracy of the gradient detection,particularly in the case of hot pixels.

The above-discussed defective pixel detection and correction techniquesimplemented by the DPDC logic 932 may be summarized by a series of flowcharts provided in FIGS. 70-72. For instance, referring first to FIG.70, a process 960 for detecting static defects is illustrated. Beginninginitially at step 962, an input pixel P is received at a first time, T₀.Next, at step 964, the location of the pixel P is compared to the valuesstored in a static defect table. Decision logic 966 determines whetherthe location of the pixel P is found in the static defect table. If thelocation of P is in the static defect table, then the process 960continues to step 968, wherein the pixel P is marked as a static defectand a replacement value is determined. As discussed above, thereplacement value may be determined based upon the value of the previouspixel (in scan order) of the same color component. The process 960 thencontinues to step 970, at which the process 960 proceeds to the dynamicand speckle detection process 980, illustrated in FIG. 71. Additionally,if at decision logic 966, the location of the pixel P is determined notto be in the static defect table, then the process 960 proceeds to step970 without performing step 968.

Continuing to FIG. 71, the input pixel P is received at time T1, asshown by step 982, for processing to determine whether a dynamic defector speckle is present. Time T1 may represent a time-shift with respectto the static defect detection process 960 of FIG. 70. As discussedabove, the dynamic defect and speckle detection process may begin afterthe static defect detection process has analyzed two scan lines (e.g.,rows) of pixels, thus allowing time for the identification of staticdefects and their respective replacement values to be determined beforedynamic/speckle detection occurs.

The decision logic 984 determines if the input pixel P was previouslymarked as a static defect (e.g., by step 968 of process 960). If P ismarked as a static defect, then the process 980 may continue to thepixel correction process shown in FIG. 72 and may bypass the rest of thesteps shown in FIG. 71. If the decision logic 984 determines that theinput pixel P is not a static defect, then the process continues to step986, and neighboring pixels are identified that may be used in thedynamic defect and speckle process. For instance, in accordance with theembodiment discussed above and illustrated in FIG. 69, the neighboringpixels may include the immediate 8 neighbors of the pixel P (e.g.,P0-P7), thus forming a 3×3 pixel area. Next, at step 988, pixel-to-pixelgradients are calculated with respect to each neighboring pixel withinthe raw frame 278, as described in Equation 51 above. Additionally, anaverage gradient (G_(av)) may be calculated as the difference betweenthe current pixel and the average of its surrounding pixels, as shown inEquations 52a and 52b.

The process 980 then branches to step 990 for dynamic defect detectionand to decision logic 998 for speckle detection. As noted above, dynamicdefect detection and speckle detection may, in some embodiments, occurin parallel. At step 990, a count C of the number of gradients that areless than or equal to the threshold dynTh is determined. As describedabove, the threshold dynTh may include fixed and dynamic components and,in one embodiment, may be determined in accordance with Equation 53above. If C is less than or equal to a maximum count, dynMaxC, then theprocess 980 continues to step 996, and the current pixel is marked asbeing a dynamic defect. Thereafter, the process 980 may continue to thepixel correction process shown in FIG. 72, which will be discussedbelow.

Returning back the branch after step 988, for speckle detection, thedecision logic 998 determines whether the average gradient G_(av) isgreater than a speckle detection threshold spkTh, which may also includea fixed and dynamic component. If G_(av) is greater than the thresholdspkTh, then the pixel P is marked as containing speckle at step 1000and, thereafter, the process 980 continues to FIG. 72 for the correctionof the speckled pixel. Further, if the output of both of the decisionlogic blocks 992 and 998 are “NO,” then this indicates that the pixel Pdoes not contain dynamic defects, speckle, or even static defects(decision logic 984). Thus, when the outputs of decision logic 992 and998 are both “NO,” the process 980 may conclude at step 994, whereby thepixel P is passed unchanged, as no defects (e.g., static, dynamic, orspeckle) were detected.

Continuing to FIG. 72, a pixel correction process 1010 in accordancewith the techniques described above is provided. At step 1012, the inputpixel P is received from process 980 of FIG. 71. It should be noted thatthe pixel P may be received by process 1010 from step 984 (staticdefect) or from steps 996 (dynamic defect) and 1000 (speckle defect).The decision logic 1014 then determines whether the pixel P is marked asa static defect. If the pixel P is a static defect, then the process1010 continues and ends at step 1016, whereby the static defect iscorrected using the replacement value determined at step 968 (FIG. 70).

If the pixel P is not identified as a static defect, then the process1010 continues from decision logic 1014 to step 1018, and directionalgradients are calculated. For instance, as discussed above withreference to Equations 58-61, the gradients may be computed as the sumof the absolute difference between the center pixel and first and secondneighboring pixels for four directions (h, v, dp, and dn). Next, at step1020, the directional gradient having the smallest value is identifiedand, thereafter, decision logic 1022 assesses whether one of the twoneighboring pixels associated with the minimum gradient is locatedoutside of the image frame (e.g., raw frame 278). If both neighboringpixels are within the image frame, then the process 1010 continues tostep 1024, and a pixel correction value (P_(C)) is determined byapplying linear interpolation to the values of the two neighboringpixels, as illustrated by Equation 62. Thereafter, the input pixel P maybe corrected using the interpolated pixel correction value P_(C), asshown at step 1030.

Returning to the decision logic 1022, if it is determined that one ofthe two neighboring pixels are located outside of the image frame (e.g.,raw frame 165), then instead of using the value of the outside pixel(Pout), the DPDC logic 932 may substitute the value of Pout with thevalue of the other neighboring pixel that is inside the image frame(Pin), as shown at step 1026. Thereafter, at step 1028, the pixelcorrection value P_(C) is determined by interpolating the values of Pinand the substituted value of Pout. In other words, in this case, P_(C)may be equivalent to the value of Pin. Concluding at step 1030, thepixel P is corrected using the value P_(C). Before continuing, it shouldbe understood that the particular defective pixel detection andcorrection processes discussed herein with reference to the DPDC logic932 are intended to reflect only one possible embodiment of the presenttechnique. Indeed, depending on design and/or cost constraints, a numberof variations are possible, and features may be added or removed suchthat the overall complexity and robustness of the defectdetection/correction logic is between the simpler detection/correctionlogic 460 implemented in the ISP front-end block 80 and the defectdetection/correction logic discussed here with reference to the DPDClogic 932.

Referring back to FIG. 68, the corrected pixel data is output from theDPDC logic 932 and then received by the noise reduction logic 934 forfurther processing. In one embodiment, the noise reduction logic 934 maybe configured to implements two-dimensional edge-adaptive low passfiltering to reduce noise in the image data while maintaining detailsand textures. The edge-adaptive thresholds may be set (e.g., by thecontrol logic 84) based upon the present lighting levels, such thatfiltering may be strengthened under low light conditions. Further, asbriefly mentioned above with regard to the determination of the dynThand spkTh values, noise variance may be determined ahead of time for agiven sensor so that the noise reduction thresholds can be set justabove noise variance, such that during the noise reduction processing,noise is reduced without significantly affecting textures and details ofthe scene (e.g., avoid/reduce false detections). Assuming a Bayer colorfilter implementation, the noise reduction logic 934 may process eachcolor component Gr, R, B, and Gb independently using a separable 7-taphorizontal filter and a 5-tap vertical filter. In one embodiment, thenoise reduction process may be carried out by correcting fornon-uniformity on the green color components (Gb and Gr), and thenperforming horizontal filtering and vertical filtering.

Green non-uniformity (GNU) is generally characterized by a slightbrightness difference between the Gr and Gb pixels given a uniformlyilluminated flat surface. Without correcting or compensating for thisnon-uniformity, certain artifacts, such as a “maze” artifact, may appearin the full color image after demosaicing. During the greennon-uniformity process may include determining, for each green pixel inthe raw Bayer image data, if the absolute difference between a currentgreen pixel (G1) and the green pixel to the right and below (G2) thecurrent pixel is less than a GNU correction threshold (gnuTh). FIG. 73illustrates the location of the G1 and G2 pixels in a 2×2 area of theBayer pattern. As shown, the color of the pixels bordering G1 may bedepending upon whether the current green pixel is a Gb or Gr pixel. Forinstance, if G1 is Gr, then G2 is Gb, the pixel to the right of G1 is R(red), and the pixel below G1 is B (blue). Alternatively, if G1 is Gb,then G2 is Gr, and the pixel to the right of G1 is B, whereas the pixelbelow G1 is R. If the absolute difference between G1 and G2 is less thanthe GNU correction threshold value, then current green pixel G1 isreplaced by the average of G1 and G2, as shown by the logic below:

$\begin{matrix}{{{if}\left( {{{abs}\left( {{G\; 1} - {G\; 2}} \right)} \leq {gnuTh}} \right)};{{G\; 1} = \frac{{G\; 1} + {G\; 2}}{2}}} & (63)\end{matrix}$

As can be appreciated, the application of green non-uniformitycorrection in this manner may help to prevent the G1 and G2 pixels frombeing averaged across edges, thus improving and/or preserving sharpness.

Horizontal filtering is applied subsequent to green non-uniformitycorrection and may, in one embodiment, provide a 7-tap horizontalfilter. Gradients across the edge of each filter tap are computed, andif it is above a horizontal edge threshold (horzTh), the filter tap isfolded to the center pixel, as will be illustrated below. The horizontalfilter may process the image data independently for each color component(R, B, Gr, Gb) and may use unfiltered values as inputs values.

By way of example, FIG. 74 shows a graphical depiction of a set ofhorizontal pixels P0 to P6, with a center tap positioned at P3. Basedupon the pixels shown in FIG. 74, edge gradients for each filter tap maybe calculated as follows:

Eh0=abs(P0−P1)  (64)

Eh1=abs(P1−P2)  (65)

Eh2=abs(P2−P3)  (66)

Eh3=abs(P3−P4)  (67)

Eh4=abs(P4−P5)  (68)

Eh5=abs(P5−P6)  (69)

The edge gradients Eh0-Eh5 may then be utilized by the horizontal filtercomponent to determine a horizontal filtering output, P_(horz), usingthe formula shown in Equation 70 below:

P _(horz)=C0×[(Eh2>horzTh[c])?P3:(Eh1>horzTh[c])?P2:(Eh0>horzTh[c])?P1:P0]+C1×[(Eh2>horzTh[c])?P3:(Eh1>horzTh[c])?P2:P1]+C2×[(Eh2>horzTh[c])?P3:P2]+C3×P3+C4×[(Eh3>horzTh[c])?P3:P4]+C5×[(Eh3>horzTh[c])?P3:(Eh4>horzTh[c])?P4:P5]+C6×[(Eh3>horzTh[c])?P3:(Eh4>horzTh[c])?P4:(Eh5>horzTh[c])?P5:P6],  (70)

wherein horzTh[c] is the horizontal edge threshold for each colorcomponent c (e.g., R, B, Gr, and Gb), and wherein C0-C6 are the filtertap coefficients corresponding to pixels P0-P6, respectively. Thehorizontal filter output P_(horz) may be applied at the center pixel P3location. In one embodiment, the filter tap coefficients C0-C6 may be16-bit two's complement values with 3 integer bits and 13 fractionalbits (3.13 in floating point). Further, it should be noted that thefilter tap coefficients C0-C6 need not necessarily be symmetrical withrespect to the center pixel P3.

Vertical filtering is also applied by the noise reduction logic 934subsequent to green non-uniformity correction and horizontal filteringprocesses. In one embodiment, the vertical filter operation may providea 5-tap filter, as shown in FIG. 75, with the center tap of the verticalfilter located at P2. The vertical filtering process may occur in asimilar manner as the horizontal filtering process described above. Forinstance, gradients across the edge of each filter tap are computed, andif it is above a vertical edge threshold (vertTh), the filter tap isfolded to the center pixel P2. The vertical filter may process the imagedata independently for each color component (R, B, Gr, Gb) and may useunfiltered values as inputs values.

Based upon the pixels shown in FIG. 75, vertical edge gradients for eachfilter tap may be calculated as follows:

Ev0=abs(P0−P1)  (71)

Ev1=abs(P1−P2)  (72)

Ev2=abs(P2−P3)  (73)

Ev3=abs(P3−P4)  (74)

The edge gradients Ev0-Ev5 may then be utilized by the vertical filterto determine a vertical filtering output, P_(vert), using the formulashown in Equation 75 below:

P _(vert)=C0×[(Ev1>vertTh[c])?P2:(Ev0>vertTh[c])?P1:P0]+C1×[(Ev1>vertTh[c])?P2:P1]+C2×P2+C3×[(Ev2>vertTh[c])?P2:P3]+C4×[(Ev2>vertTh[c])?P2:(Eh3>vertTh[c])?P3:P4],  (75)

wherein vertTh[c] is the vertical edge threshold for each colorcomponent c (e.g., R, B, Gr, and Gb), and wherein C0-C4 are the filtertap coefficients corresponding to the pixels P0-P4 of FIG. 75,respectively. The vertical filter output P_(vert) may be applied at thecenter pixel P2 location. In one embodiment, the filter tap coefficientsC0-C4 may be 16-bit two's complement values with 3 integer bits and 13fractional bits (3.13 in floating point). Further, it should be notedthat the filter tap coefficients C0-C4 need not necessarily besymmetrical with respect to the center pixel P2.

Additionally, with regard to boundary conditions, when neighboringpixels are outside of the raw frame 278 (FIG. 19), the values of theout-of-bound pixels are replicated with the value of same color pixel atthe edge of the raw frame. This convention may be implemented for bothhorizontal and vertical filtering operations. By way of example,referring again to FIG. 74, in the case of horizontal filtering, if thepixel P2 is an edge pixel at the left-most edge of the raw frame, andthe pixels P0 and P1 are outside of the raw frame, then the values ofthe pixels P0 and P1 are substituted with the value of the pixel P2 forhorizontal filtering.

Referring again back to the block diagram of the raw processing logic900 shown in FIG. 68, the output of the noise reduction logic 934 issubsequently sent to the lens shading correction (LSC) logic 936 forprocessing. As discussed above, lens shading correction techniques mayinclude applying an appropriate gain on a per-pixel basis to compensatefor drop-offs in light intensity, which may be the result of thegeometric optics of the lens, imperfections in manufacturing,misalignment of the microlens array and the color array filter, and soforth. Further, the infrared (IR) filter in some lenses may cause thedrop-off to be illuminant-dependent and, thus, lens shading gains may beadapted depending upon the light source detected.

In the depicted embodiment, the LSC logic 936 of the ISP pipe 82 may beimplemented in a similar manner, and thus provide generally the samefunctions, as the LSC logic 464 of the ISP front-end block 80, asdiscussed above with reference to FIGS. 40-48. Accordingly, in order toavoid redundancy, it should be understood that the LSC logic 936 of thepresently illustrated embodiment is configured to operate in generallythe same manner as the LSC logic 460 and, as such, the description ofthe lens shading correction techniques provided above will not berepeated here. However, to generally summarize, it should be understoodthat the LSC logic 936 may process each color component of the raw pixeldata stream independently to determine a gain to apply to the currentpixel. In accordance with the above-discussed embodiments, the lensshading correction gain may be determined based upon a defined set ofgain grid points distributed across the imaging frame, wherein theinterval between each grid point is defined by a number of pixels (e.g.,8 pixels, 16 pixels etc.). If the location of the current pixelcorresponds to a grid point, then the gain value associated with thatgrid point is applied to the current pixel. However, if the location ofthe current pixel is between grid points (e.g., G0, G1, G2, and G3 ofFIG. 43), then the LSC gain value may be calculated by interpolation ofthe grid points between which the current pixel is located (Equations13a and 13b). This process is depicted by the process 528 of FIG. 44.Further, as mentioned above with respect to FIG. 42, in someembodiments, the grid points may be distributed unevenly (e.g.,logarithmically), such that the grid points are less concentrated in thecenter of the LSC region 504, but more concentrated towards the cornersof the LSC region 504, typically where lens shading distortion is morenoticeable.

Additionally, as discussed above with reference to FIGS. 47 and 48, theLSC logic 936 may also apply a radial gain component with the grid gainvalues. The radial gain component may be determined based upon distanceof the current pixel from the center of the image (Equations 14-16). Asmentioned, using a radial gain allows for the use of single common gaingrid for all color components, which may greatly reduce the totalstorage space required for storing separate gain grids for each colorcomponent. This reduction in grid gain data may decrease implementationcosts, as grid gain data tables may account for a significant portion ofmemory or chip area in image processing hardware.

Next, referring again to the raw processing logic block diagram 900 ofFIG. 68, the output of the LSC logic 936 is then passed to a secondgain, offset, and clamping (GOC) block 938. The GOC logic 938 may beapplied prior to demosaicing (by logic block 940) and may be used toperform auto-white balance on the output of the LSC logic 936. In thedepicted embodiment, the GOC logic 938 may be implemented in the samemanner as the GOC logic 930 (and the BLC logic 462). Thus, in accordancewith the Equation 11 above, the input received by the GOC logic 938 isfirst offset by a signed value and then multiplied by a gain. Theresulting value is then clipped to a minimum and a maximum range inaccordance with Equation 12.

Thereafter, the output of the GOC logic 938 is forwarded to thedemosaicing logic 940 for processing to produce a full color (RGB) imagebased upon the raw Bayer input data. As will be appreciated, the rawoutput of an image sensor using a color filter array, such as a Bayerfilter is “incomplete” in the sense that each pixel is filtered toacquire only a single color component. Thus, the data collected for anindividual pixel alone is insufficient to determine color. Accordingly,demosaicing techniques may be used to generate a full color image fromthe raw Bayer data by interpolating the missing color data for eachpixel.

Referring now to FIG. 76, a graphical process flow 692 that provides ageneral overview as to how demosaicing may be applied to a raw Bayerimage pattern 1034 to produce a full color RGB is illustrated. As shown,a 4×4 portion 1036 of the raw Bayer image 1034 may include separatechannels for each color component, including a green channel 1038, a redchannel 1040, and a blue channel 1042. Because each imaging pixel in aBayer sensor only acquires data for one color, the color data for eachcolor channel 1038, 1040, and 1042 may be incomplete, as indicated bythe “?” symbols. By applying a demosaicing technique 1044, the missingcolor samples from each channel may be interpolated. For instance, asshown by reference number 1046, interpolated data G′ may be used to fillthe missing samples on the green color channel Similarly, interpolateddata R′ may (in combination with the interpolated data G′ 1046) be usedto fill the missing samples on the red color channel 1048, andinterpolated data B′may (in combination with the interpolated data G′1046) be used to fill the missing samples on the blue color channel1050. Thus, as a result of the demosaicing process, each color channel(R, G, B) will have a full set of color data, which may then be used toreconstruct a full color RGB image 1052.

A demosaicing technique that may be implemented by the demosaicing logic940 will now be described in accordance with one embodiment. On thegreen color channel, missing color samples may be interpolated using alow pass directional filter on known green samples and a high pass (orgradient) filter on the adjacent color channels (e.g., red and blue).For the red and blue color channels, the missing color samples may beinterpolated in a similar manner, but by using low pass filtering onknown red or blue values and high pass filtering on co-locatedinterpolated green values. Further, in one embodiment, demosaicing onthe green color channel may utilize a 5×5 pixel block edge-adaptivefilter based on the original Bayer color data. As will be discussedfurther below, the use of an edge-adaptive filter may provide for thecontinuous weighting based on gradients of horizontal and verticalfiltered values, which reduce the appearance of certain artifacts, suchas aliasing, “checkerboard,” or “rainbow” artifacts, commonly seen inconventional demosaicing techniques.

During demosaicing on the green channel, the original values for thegreen pixels (Gr and Gb pixels) of the Bayer image pattern are used.However, in order to obtain a full set of data for the green channel,green pixel values may be interpolated at the red and blue pixels of theBayer image pattern. In accordance with the present technique,horizontal and vertical energy components, respectively referred to asEh and Ev, are first calculated at red and blue pixels based on theabove-mentioned 5×5 pixel block. The values of Eh and Ev may be used toobtain an edge-weighted filtered value from the horizontal and verticalfiltering steps, as discussed further below.

By way of example, FIG. 77 illustrates the computation of the Eh and Evvalues for a red pixel centered in the 5×5 pixel block at location (j,i), wherein j corresponds to a row and i corresponds to a column. Asshown, the calculation of Eh considers the middle three rows (j−1, j,j+1) of the 5×5 pixel block, and the calculation of Ev considers themiddle three columns (i−1, i, i+1) of the 5×5 pixel block. To computeEh, the absolute value of the sum of each of the pixels in the redcolumns (i−2, i, i+2) multiplied by a corresponding coefficient (e.g.,−1 for columns i−2 and i+2; 2 for column i) is summed with the absolutevalue of the sum of each of the pixels in the blue columns (i−1, i+1)multiplied by a corresponding coefficient (e.g., 1 for column i−1; −1for column i+1). To compute Ev, the absolute value of the sum of each ofthe pixels in the red rows (j−2, j, j+2) multiplied by a correspondingcoefficient (e.g., −1 for rows j−2 and j+2; 2 for row j) is summed withthe absolute value of the sum of each of the pixels in the blue rows(j−1, j+1) multiplied by a corresponding coefficient (e.g., 1 for rowj−1; −1 for row j+1). These computations are illustrated by Equations 76and 77 below:

Eh=abs[2((P(j−1,i)+P(j,i)+P(j+1,i))−(P(j−1,i−2)+P(j,i−2)+P(j+1,i−2))−(P(j−1,i+2)+P(j,i+2)+P(j+1,i+2)]+abs[(P(j−1,i−1)+P(j,i−1)+P(j+1,i−1))−(P(j−1,i+1)+P(j,i+1)+P(j+1,i+1)]  (76)

Ev=abs[2(P(j,i−1)+P(j,i)+P(j,i+1))−(P(j−2,i−1)+P(j−2,i)+P(j−2,i+1))−(P(j+2,i−1)+P(j+2,i)+P(j+2,i+1]+abs[(P(j−1,i−1)+P(j−1,i)+P(j−1,i+1))−(P(j+1,i−1)+P(j+1,i)+P(j+1,i+1)]  (77)

Thus, the total energy sum may be expressed as: Eh+Ev. Further, whilethe example shown in FIG. 77 illustrates the computation of Eh and Evfor a red center pixel at (j, i), it should be understood that the Ehand Ev values may be determined in a similar manner for blue centerpixels.

Next, horizontal and vertical filtering may be applied to the Bayerpattern to obtain the vertical and horizontal filtered values Gh and Gv,which may represent interpolated green values in the horizontal andvertical directions, respectively. The filtered values Gh and Gv may bedetermined using a low pass filter on known neighboring green samples inaddition to using directional gradients of the adjacent color (R or B)to obtain a high frequency signal at the locations of the missing greensamples. For instance, with reference to FIG. 78, an example ofhorizontal interpolation for determining Gh will now be illustrated.

As shown in FIG. 78, five horizontal pixels (R0, G1, R2, G3, and R4) ofa red line 1060 of the Bayer image, wherein R2 is assumed to be thecenter pixel at (j, i), may be considered in determining Gh. Filteringcoefficients associated with each of these five pixels are indicated byreference numeral 1062. Accordingly, the interpolation of a green value,referred to as G2′, for the center pixel R2, may be determined asfollows:

$\begin{matrix}{{G\; 2^{\prime}} = {\frac{{G\; 1} + {G\; 3}}{2} + \frac{{2R\; 2} - \left( \frac{{R\; 0} + {R\; 2}}{2} \right) - \left( \frac{{R\; 2} + {R\; 4}}{2} \right)}{2}}} & (78)\end{matrix}$

Various mathematical operations may then be utilized to produce theexpression for G2′ shown in Equations 79 and 80 below:

$\begin{matrix}{{G\; 2^{\prime}} = {\frac{{2G\; 1} + {2G\; 3}}{4} + \frac{{4R\; 2} - {R\; 0} - {R\; 2} - {R\; 2} - {R\; 4}}{4}}} & (79) \\{{G\; 2^{\prime}} = \frac{{2G\; 1} + {2G\; 3} + {2R\; 2} - {R\; 0} - {R\; 4}}{4}} & (80)\end{matrix}$

Thus, with reference to FIG. 78 and the Equations 78-80 above, thegeneral expression for the horizontal interpolation for the green valueat (j, i) may be derived as:

$\begin{matrix}{{Gh} = \frac{\begin{pmatrix}{{2{P\left( {j,{i - 1}} \right)}} + {2{P\left( {j,{i + 1}} \right)}} + {2P\left( {j,i} \right)} -} \\{{P\left( {j,{i - 2}} \right)} - {P\left( {j,{i + 2}} \right)}}\end{pmatrix}}{4}} & (81)\end{matrix}$

The vertical filtering component Gv may be determined in a similarmanner as Gh. For example, referring to FIG. 79, five vertical pixels(R0, G1, R2, G3, and R4) of a red column 1064 of the Bayer image andtheir respective filtering coefficients 1068, wherein R2 is assumed tobe the center pixel at (j, i), may be considered in determining Gv.Using low pass filtering on the known green samples and high passfiltering on the red channel in the vertical direction, the followingexpression may be derived for Gv:

$\begin{matrix}{{Gv} = \frac{\begin{pmatrix}{{2{P\left( {{j - 1},i} \right)}} + {2{P\left( {{j + 1},i} \right)}} + {2{P\left( {j,i} \right)}} -} \\{{P\left( {{j - 2},i} \right)} - {P\left( {{j + 2},i} \right)}}\end{pmatrix}}{4}} & (82)\end{matrix}$

While the examples discussed herein have shown the interpolation ofgreen values on a red pixel, it should be understood that theexpressions set forth in Equations 81 and 82 may also be used in thehorizontal and vertical interpolation of green values for blue pixels.

The final interpolated green value G′ for the center pixel (j, i) may bedetermined by weighting the horizontal and vertical filter outputs (Ghand Gv) by the energy components (Eh and Ev) discussed above to yieldthe following equation:

$\begin{matrix}{{G^{\prime}\left( {j,i} \right)} = {{\left( \frac{Ev}{{Eh} + {Ev}} \right){Gh}} + {\left( \frac{Eh}{{Eh} + {Ev}} \right){Gv}}}} & (83)\end{matrix}$

As discussed above, the energy components Eh and Ev may provide foredge-adaptive weighting of the horizontal and vertical filter outputs Ghand Gv, which may help to reduce image artifacts, such as rainbow,aliasing, or checkerboard artifacts, in the reconstructed RGB image.Additionally, the demosaicing logic 940 may provide an option to bypassthe edge-adaptive weighting feature by setting the Eh and Ev values eachto 1, such that Gh and Gv are equally weighted.

In one embodiment, the horizontal and vertical weighting coefficients,shown in Equation 51 above, may be quantized to reduce the precision ofthe weighting coefficients to a set of “coarse” values. For instance, inone embodiment, the weighting coefficients may be quantized to eightpossible weight ratios: 1/8, 2/8, 3/8, 4/8, 5/8, 6/8, 7/8, and 8/8.Other embodiments may quantize the weighting coefficients into 16 values(e.g., 1/16 to 16/16), 32 values (1/32 to 32/32), and so forth. As canbe appreciated, when compared to using full precision values (e.g.,32-bit floating point values), the quantization of the weightcoefficients may reduce the implementation complexity when determiningand applying the weighting coefficients to horizontal and verticalfilter outputs.

In further embodiments, the presently disclosed techniques, in additionto determining and using horizontal and vertical energy components toapply weighting coefficients to the horizontal (Gh) and vertical (Gv)filtered values, may also determine and utilize energy components in thediagonal-positive and diagonal-negative directions. For instance, insuch embodiments, filtering may also be applied in the diagonal-positiveand diagonal-negative directions. Weighting of the filter outputs mayinclude selecting the two highest energy components, and using theselected energy components to weight their respective filter outputs.For example, assuming that the two highest energy components correspondto the vertical and diagonal-positive directions, the vertical anddiagonal-positive energy components are used to weight the vertical anddiagonal-positive filter outputs to determine the interpolated greenvalue (e.g., at a red or blue pixel location in the Bayer pattern).

Next, demosaicing on the red and blue color channels may be performed byinterpolating red and blue values at the green pixels of the Bayer imagepattern, interpolating red values at the blue pixels of the Bayer imagepattern, and interpolating blue values at the red pixels of the Bayerimage pattern. In accordance with the present discussed techniques,missing red and blue pixel values may be interpolated using low passfiltering based upon known neighboring red and blue pixels and high passfiltering based upon co-located green pixel values, which may beoriginal or interpolated values (from the green channel demosaicingprocess discussed above) depending on the location of the current pixel.Thus, with regard to such embodiments, it should be understood thatinterpolation of missing green values may be performed first, such thata complete set of green values (both original and interpolated values)is available when interpolating the missing red and blue samples.

The interpolation of red and blue pixel values may be described withreference to FIG. 80, which illustrates various 3×3 blocks of the Bayerimage pattern to which red and blue demosaicing may be applied, as wellas interpolated green values (designated by G′) that may have beenobtained during demosaicing on the green channel. Referring first toblock 1070, the interpolated red value, R′₁₁, for the Gr pixel (G₁₁) maybe determined as follows:

$\begin{matrix}{{R_{11}^{\prime} = {\frac{\left( {R_{10} + R_{12}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{10}^{\prime} - G_{12}^{\prime}} \right)}{2}}},} & (84)\end{matrix}$

where G′₁₀ and G′₁₂ represent interpolated green values, as shown byreference number 1078.

Similarly, the interpolated blue value, B′₁₁, for the Gr pixel (G₁₁) maybe determined as follows:

$\begin{matrix}{{B_{11}^{\prime} = {\frac{\left( {B_{01} + B_{21}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{01}^{\prime} - G_{21}^{\prime}} \right)}{2}}},} & (85)\end{matrix}$

wherein G′₀₁ and G′₂₁ represent interpolated green values (1078).

Next, referring to the pixel block 1072, in which the center pixel is aGb pixel (G₁₁), the interpolated red value, R′₁₁, and blue value B′₁₁,may be determined as shown in Equations 86 and 87 below:

$\begin{matrix}{R_{11}^{\prime} = {\frac{\left( {R_{01} + R_{21}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{01}^{\prime} - G_{21}^{\prime}} \right)}{2}}} & (86) \\{B_{11}^{\prime} = {\frac{\left( {B_{10} + B_{12}} \right)}{2} + \frac{\left( {{2\; G_{11}} - G_{10}^{\prime} - G_{12}^{\prime}} \right)}{2}}} & (87)\end{matrix}$

Further, referring to pixel block 1074, the interpolation of a red valueon a blue pixel, B₁₁, may be determined as follows:

$\begin{matrix}{{R_{11}^{\prime} = {\frac{\left( {R_{00} + R_{02} + R_{20} + R_{22}} \right)}{4} + \frac{\left( {{4\; G_{11}^{\prime}} - G_{00}^{\prime} - G_{02}^{\prime} - G_{20}^{\prime} - G_{22}^{\prime}} \right)}{4}}},} & (88)\end{matrix}$

wherein G′₀₀, G′₀₂, G′₁₁, G′₂₀, and G′₂₂ represent interpolated greenvalues, as shown by reference number 1080. Finally, the interpolation ofa blue value on a red pixel, as shown by pixel block 1076, may becalculated as follows:

$\begin{matrix}{B_{11}^{\prime} = {\frac{\left( {B_{00} + B_{02} + B_{20} + B_{22}} \right)}{4} + \frac{\left( {{4\; G_{11}^{\prime}} - G_{00}^{\prime} - G_{02}^{\prime} - G_{20}^{\prime} - G_{22}^{\prime}} \right)}{4}}} & (89)\end{matrix}$

While the embodiment discussed above relied on color differences (e.g.,gradients) for determining red and blue interpolated values, anotherembodiment may provide for interpolated red and blue values using colorratios. For instance, interpolated green values (blocks 1078 and 1080)may be used to obtain a color ratio at red and blue pixel locations ofthe Bayer image pattern, and linear interpolation of the ratios may beused to determine an interpolated color ratio for the missing colorsample. The green value, which may be an interpolated or an originalvalue, may be multiplied by the interpolated color ratio to obtain afinal interpolated color value. For instance, interpolation of red andblue pixel values using color ratios may be performed in accordance withthe formulas below, wherein Equations 90 and 91 show the interpolationof red and blue values for a Gr pixel, Equations 92 and 93 show theinterpolation of red and blue values for a Gb pixel, Equation 94 showsthe interpolation of a red value on a blue pixel, and Equation 95 showsthe interpolation of a blue value on a red pixel:

$\begin{matrix}{{R_{11}^{\prime} = {G_{11}\frac{\left( \frac{R_{10}}{G_{10}^{\prime}} \right) + \left( \frac{R_{12}}{G_{12}^{\prime}} \right)}{2}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gr}\mspace{14mu} {pixel}} \right)} & (90) \\{{B_{11}^{\prime} = {G_{11}\frac{\left( \frac{B_{01}}{G_{01}^{\prime}} \right) + \left( \frac{B_{21}}{G_{21}^{\prime}} \right)}{2}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gr}\mspace{14mu} {pixel}} \right)} & (91) \\{{R_{11}^{\prime} = {G_{11}\frac{\left( \frac{R_{01}}{G_{01}^{\prime}} \right) + \left( \frac{R_{21}}{G_{21}^{\prime}} \right)}{2}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gb}\mspace{14mu} {pixel}} \right)} & (92) \\{{B_{11}^{\prime} = {G_{11}\frac{\left( \frac{B_{10}}{G_{10}^{\prime}} \right) + \left( \frac{B_{12}}{G_{12}^{\prime}} \right)}{2}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {when}\mspace{14mu} G_{11}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {Gb}\mspace{14mu} {pixel}} \right)} & (93) \\{{R_{11}^{\prime} = {G_{11}^{\prime}\frac{\left( \frac{R_{00}}{G_{00}^{\prime}} \right) + \left( \frac{R_{02}}{G_{02}^{\prime}} \right) + \left( \frac{R_{20}}{G_{20}^{\prime}} \right) + \left( \frac{R_{22}}{G_{22}^{\prime}} \right)}{4}}}\left( {R_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {on}\mspace{14mu} a\mspace{14mu} {blue}\mspace{14mu} {pixel}\mspace{14mu} B_{11}} \right)} & (94) \\{{B_{11}^{\prime} = {G_{11}^{\prime}\frac{\left( \frac{B_{00}}{G_{00}^{\prime}} \right) + \left( \frac{B_{02}}{G_{02}^{\prime}} \right) + \left( \frac{B_{20}}{G_{20}^{\prime}} \right) + \left( \frac{B_{22}}{G_{22}^{\prime}} \right)}{4}}}\left( {B_{11}^{\prime}\mspace{14mu} {interpolated}\mspace{14mu} {on}\mspace{14mu} a\mspace{14mu} {red}\mspace{14mu} {pixel}\mspace{14mu} B_{11}} \right)} & (95)\end{matrix}$

Once the missing color samples have been interpolated for each imagepixel from the Bayer image pattern, a complete sample of color valuesfor each of the red, blue, and green color channels (e.g., 1046, 1048,and 1050 of FIG. 76) may be combined to produce a full color RGB image.For instance, referring back FIGS. 49 and 50, the output 910 of the rawpixel processing logic 900 may be an RGB image signal in 8, 10, 12 or14-bit formats.

Referring now to FIGS. 81-84, various flow charts illustrating processesfor demosaicing a raw Bayer image pattern in accordance with disclosedembodiments are illustrated. Specifically, the process 1082 of FIG. 81depicts the determination of which color components are to beinterpolated for a given input pixel P. Based on the determination byprocess 1082, one or more of the process 1100 (FIG. 82) forinterpolating a green value, the process 1112 (FIG. 83) forinterpolating a red value, or the process 1124 (FIG. 84) forinterpolating a blue value may be performed (e.g., by the demosaicinglogic 940).

Beginning with FIG. 81, the process 1082 begins at step 1084 when aninput pixel P is received. Decision logic 1086 determines the color ofthe input pixel. For instance, this may depend on the location of thepixel within the Bayer image pattern. Accordingly, if P is identified asbeing a green pixel (e.g., Gr or Gb), the process 1082 proceeds to step1088 to obtain interpolated red and blue values for P. This may include,for example, continuing to the processes 1112 and 1124 of FIGS. 83 and84, respectively. If P is identified as being a red pixel, then theprocess 1082 proceeds to step 1090 to obtain interpolated green and bluevalues for P. This may include further performing the processes 1100 and1124 of FIGS. 82 and 84, respectively. Additionally, if P is identifiedas being a blue pixel, then the process 1082 proceeds to step 1092 toobtain interpolated green and red values for P. This may include furtherperforming the processes 1100 and 1112 of FIGS. 82 and 83, respectively.Each of the processes 1100, 1112, and 1124 are described further below.

The process 1100 for determining an interpolated green value for theinput pixel P is illustrated in FIG. 82 and includes steps 1102-1110. Atstep 1102, the input pixel P is received (e.g., from process 1082).Next, at step 1104, a set of neighboring pixels forming a 5×5 pixelblock is identified, with P being the center of the 5×5 block.Thereafter, the pixel block is analyzed to determine horizontal andvertical energy components at step 1106. For instance, the horizontaland vertical energy components may be determined in accordance withEquations 76 and 77 for calculating Eh and Ev, respectively. Asdiscussed, the energy components Eh and Ev may be used as weightingcoefficients to provide edge-adaptive filtering and, therefore, reducethe appearance of certain demosaicing artifacts in the final image. Atstep 1108, low pass filtering and high pass filtering as applied inhorizontal and vertical directions to determine horizontal and verticalfiltering outputs. For example, the horizontal and vertical filteringoutputs, Gh and Gv, may be calculated in accordance with Equations 81and 82. Next the process 1082 continues to step 1110, at which theinterpolated green value G′ is interpolated based on the values of Ghand Gv weighted with the energy components Eh and Ev, as shown inEquation 83.

Next, with regard to the process 1112 of FIG. 83, the interpolation ofred values may begin at step 1114, at which the input pixel P isreceived (e.g., from process 1082). At step 1116, a set of neighboringpixels forming a 3×3 pixel block is identified, with P being the centerof the 3×3 block. Thereafter, low pass filtering is applied onneighboring red pixels within the 3×3 block at step 1118, and high passfiltering is applied (step 1120) on co-located green neighboring values,which may be original green values captured by the Bayer image sensor,or interpolated values (e.g., determined via process 1100 of FIG. 82).The interpolated red value R′ for P may be determined based on the lowpass and high pass filtering outputs, as shown at step 1122. Dependingon the color of P, R′ may be determined in accordance with one of theEquations 84, 86, or 88.

With regard to the interpolation of blue values, the process 1124 ofFIG. 84 may be applied. The steps 1126 and 1128 are generally identicalto the steps 1114 and 1116 of the process 1112 (FIG. 83). At step 1130,low pass filtering is applied on neighboring blue pixels within the 3×3,and, at step 1132, high pass filtering is applied on co-located greenneighboring values, which may be original green values captured by theBayer image sensor, or interpolated values (e.g., determined via process1100 of FIG. 82). The interpolated blue value B′ for P may be determinedbased on the low pass and high pass filtering outputs, as shown at step1134. Depending on the color of P, B′ may be determined in accordancewith one of the Equations 85, 87, or 89. Further, as mentioned above,the interpolation of red and blue values may be determined using colordifferences (Equations 84-89) or color ratios (Equations 90-95). Again,it should be understood that interpolation of missing green values maybe performed first, such that a complete set of green values (bothoriginal and interpolated values) is available when interpolating themissing red and blue samples. For example, the process 1100 of FIG. 82may be applied to interpolate all missing green color samples beforeperforming the processes 1112 and 1124 of FIGS. 83 and 84, respectively.

Referring to FIGS. 85-88, examples of colored drawings of imagesprocessed by the raw pixel processing logic 900 in the ISP pipe 82 areprovided. FIG. 85 depicts an original image scene 1140, which may becaptured by the image sensor 90 of the imaging device 30. FIG. 86 showsa raw Bayer image 1142 which may represent the raw pixel data capturedby the image sensor 90. As mentioned above, conventional demosaicingtechniques may not provide for adaptive filtering based on the detectionof edges (e.g., borders between areas of two or more colors) in theimage data, which may, undesirably, produce artifacts in the resultingreconstructed full color RGB image. For instance, FIG. 87 shows an RGBimage 1144 reconstructed using conventional demosaicing techniques, andmay include artifacts, such as “checkerboard” artifacts 1146 at the edge1148. However, comparing the image 1144 to the RGB image 1150 of FIG.88, which may be an example of an image reconstructed using thedemosaicing techniques described above, it can be seen that thecheckerboard artifacts 1146 present in FIG. 87 are not present, or atleast their appearance is substantially reduced at the edge 1148. Thus,the images shown in FIGS. 85-88 are intended to illustrate at least oneadvantage that the demosaicing techniques disclosed herein have overconventional methods.

Referring back to FIG. 67, having now thoroughly described the operationof the raw pixel processing logic 900, which may output an RGB imagesignal 910, the present discussion will now focus on describing theprocessing of the RGB image signal 910 by the RGB processing logic 902.As shown the RGB image signal 910 may be sent to the selection logic 914and/or to the memory 108. The RGB processing logic 902 may receive theinput signal 916, which may be RGB image data from the signal 910 orfrom the memory 108, as shown by signal 912, depending on theconfiguration of the selection logic 914. The RGB image data 916 may beprocessed by the RGB processing logic 902 to perform color adjustmentsoperations, including color correction (e.g., using a color correctionmatrix), the application of color gains for auto-white balancing, aswell as global tone mapping, and so forth.

A block diagram depicting a more detailed view of an embodiment of theRGB processing logic 902 is illustrated in FIG. 89. As shown, the RGBprocessing logic 902 includes the gain, offset, and clamping (GOC) logic1160, the RGB color correction logic 1162, the GOC logic 1164, the RGBgamma adjustment logic, and the color space conversion logic 1168. Theinput signal 916 is first received by the gain, offset, and clamping(GOC) logic 1160. In the illustrated embodiment, the GOC logic 1160 mayapply gains to perform auto-white balancing on one or more of the R, G,or B color channels before processing by the color correction logic1162.

The GOC logic 1160 may be similar to the GOC logic 930 of the raw pixelprocessing logic 900, except that the color components of the RGB domainare processed, rather the R, B, Gr, and Gb components of the Bayer imagedata. In operation, the input value for the current pixel is firstoffset by a signed value O[c] and multiplied by a gain G[c], as shown inEquation 11 above, wherein c represents the R, G, and B. As discussedabove, the gain G[c] may be a 16-bit unsigned number with 2 integer bitsand 14 fraction bits (e.g., 2.14 floating point representation), and thevalues for the gain G[c] may be previously determined during statisticsprocessing (e.g., in the ISP front-end block 80). The computed pixelvalue Y (based on Equation 11) is then be clipped to a minimum and amaximum range in accordance with Equation 12. As discussed above, thevariables min[c] and max[c] may represent signed 16-bit “clippingvalues” for the minimum and maximum output values, respectively. In oneembodiment, the GOC logic 1160 may also be configured to maintain acount of the number of pixels that were clipped above and below maximumand minimum, respectively, for each color component R, G, and B.

The output of the GOC logic 1160 is then forwarded to the colorcorrection logic 1162. In accordance with the presently disclosedtechniques, the color correction logic 1162 may be configured to applycolor correction to the RGB image data using a color correction matrix(CCM). In one embodiment, the CCM may be a 3×3 RGB transform matrix,although matrices of other dimensions may also be utilized in otherembodiments (e.g., 4×3, etc.). Accordingly, the process of performingcolor correction on an input pixel having R, G, and B components may beexpressed as follows:

$\begin{matrix}{{\begin{bmatrix}R^{\prime} & G^{\prime} & B^{\prime}\end{bmatrix} = {\begin{bmatrix}{{CCM}\; 00} & {{CCM}\; 01} & {{CCM}\; 02} \\{{CCM}\; 10} & {{CCM}\; 11} & {{CCM}\; 12} \\{{CCM}\; 20} & {{CCM}\; 21} & {{CCM}\; 22}\end{bmatrix} \times \begin{bmatrix}R & G & B\end{bmatrix}}},} & (96)\end{matrix}$

wherein R, G, and B represent the current red, green, and blue valuesfor the input pixel, CCM00-CCM22 represent the coefficients of the colorcorrection matrix, and R′, G′, and B′ represent the corrected red,green, and blue values for the input pixel. Accordingly, the correctcolor values may be computed in accordance with Equations 97-99 below:

R′=(CCM00×R)+(CCM01×G)+(CCM02×B)  (97)

G′=(CCM10×R)+(CCM11×G)+(CCM12×B)  (98)

B′=(CCM20×R)+(CCM21×G)+(CCM22×B)  (99)

The coefficients (CCM00-CCM22) of the CCM may be determined duringstatistics processing in the ISP front-end block 80, as discussed above.In one embodiment, the coefficients for a given color channel may beselected such that the sum of those coefficients (e.g., CCM00, CCM01,and CCM02 for red color correction) is equal to 1, which may help tomaintain the brightness and color balance. Further, the coefficients aretypically selected such that a positive gain is applied to the colorbeing corrected. For instance, with red color correction, thecoefficient CCM00 may be greater than 1, while one or both of thecoefficients CCM01 and CCM02 may be less than 1. Setting thecoefficients in this manner may enhance the red (R) component in theresulting corrected R′ value while subtracting some of the blue (B) andgreen (G) component. As will be appreciated, this may address issueswith color overlap that may occur during acquisition of the originalBayer image, as a portion of filtered light for a particular coloredpixel may “bleed” into a neighboring pixel of a different color. In oneembodiment, the coefficients of the CCM may be provided as 16-bittwo's-complement numbers with 4 integer bits and 12 fraction bits(expressed in floating point as 4.12). Additionally, the colorcorrection logic 1162 may provide for clipping of the computed correctedcolor values if the values exceed a maximum value or are below a minimumvalue.

The output of the RGB color correction logic 1162 is then passed toanother GOC logic block 1164. The GOC logic 1164 may be implemented inan identical manner as the GOC logic 1160 and, thus, a detaileddescription of the gain, offset, and clamping functions provided willnot be repeated here. In one embodiment, the application of the GOClogic 1164 subsequent to color correction may provide for auto-whitebalance of the image data based on the corrected color values, and mayalso adjust sensor variations of the red-to-green and blue-to-greenratios.

Next, the output of the GOC logic 1164 is sent to the RGB gammaadjustment logic 1166 for further processing. For instance, the RGBgamma adjustment logic 1166 may provide for gamma correction, tonemapping, histogram matching, and so forth. In accordance with disclosedembodiments, the gamma adjustment logic 1166 may provide for a mappingof the input RGB values to corresponding output RGB values. Forinstance, the gamma adjustment logic may provide for a set of threelookup tables, one table for each of the R, G, and B components. By wayof example, each lookup table may be configured to store 256 entries of10-bit values, each value representing an output level. The tableentries may be evenly distributed in the range of the input pixelvalues, such that when the input value falls between two entries, theoutput value may be linearly interpolated. In one embodiment, each ofthe three lookup tables for R, G, and B may be duplicated, such that thelookup tables are “double buffered” in memory, thus allowing for onetable to be used during processing, while its duplicate is beingupdated. Based on the 10-bit output values discussed above, it should benoted that the 14-bit RGB image signal is effectively down-sampled to 10bits as a result of the gamma correction process in the presentembodiment.

The output of the gamma adjustment logic 1166 may be sent to the memory108 and/or to the color space conversion logic 1168. The color spaceconversion (CSC) logic 1168 may be configured to convert the RGB outputfrom the gamma adjustment logic 1166 to the YCbCr format, in which Yrepresents a luma component, Cb represents a blue-difference chromacomponent, and Cr represents a red-difference chroma component, each ofwhich may be in a 10-bit format as a result of bit-depth conversion ofthe RGB data from 14-bits to 10-bits during the gamma adjustmentoperation. As discussed above, in one embodiment, the RGB output of thegamma adjustment logic 1166 may be down-sampled to 10-bits and thusconverted to 10-bit YCbCr values by the CSC logic 1168, which may thenbe forwarded to the YCbCr processing logic 904, which will be discussedfurther below.

The conversion from the RGB domain to the YCbCr color space may beperformed using a color space conversion matrix (CSCM). For instance, inone embodiment, the CSCM may be a 3×3 transform matrix. The coefficientsof the CSCM may be set in accordance with a known conversion equation,such as the BT.601 and BT.709 standards. Additionally, the CSCMcoefficients may be flexible based on the desired range of input andoutputs. Thus, in some embodiments, the CSCM coefficients may bedetermined and programmed based on data collected during statisticsprocessing in the ISP front-end block 80.

The process of performing YCbCr color space conversion on an RGB inputpixel may be expressed as follows:

$\begin{matrix}{{\begin{bmatrix}Y & {Cb} & {Cr}\end{bmatrix} = {\begin{bmatrix}{{CSCM}\; 00} & {{CSCM}\; 01} & {{CSCM}\; 02} \\{{CSCM}\; 10} & {{CSCM}\; 11} & {{CSCM}\; 12} \\{{CSCM}\; 20} & {{CSCM}\; 21} & {{CSCM}\; 22}\end{bmatrix} \times \begin{bmatrix}R & G & B\end{bmatrix}}},} & (100)\end{matrix}$

wherein R, G, and B represent the current red, green, and blue valuesfor the input pixel in 10-bit form (e.g., as processed by the gammaadjustment logic 1166), CSCM00-CSCM22 represent the coefficients of thecolor space conversion matrix, and Y, Cb, and Cr represent the resultingluma, and chroma components for the input pixel. Accordingly, the valuesfor Y, Cb, and Cr may be computed in accordance with Equations 101-103below:

Y=(CSCM00×R)+(CSCM01×G)+(CSCM02×B)  (101)

Cb=(CSCM10×R)+(CSCM11×G)+(CSCM12×B)  (102)

Following the color space conversion operation, the resulting YCbCrvalues may be output from the CSC logic 1168 as the signal 918, whichmay be processed by the YCbCr processing logic 904, as will be discussedbelow.

In one embodiment, the coefficients of the CSCM may be 16-bittwo's-complement numbers with 4 integer bits and 12 fraction bits(4.12). In another embodiment, the CSC logic 1168 may further beconfigured to apply an offset to each of the Y, Cb, and Cr values, andto clip the resulting values to a minimum and maximum value. By way ofexample only, assuming that the YCbCr values are in 10-bit form, theoffset may be in a range of −512 to 512, and the minimum and maximumvalues may be 0 and 1023, respectively.

Referring again back to the block diagram of the ISP pipe logic 82 inFIG. 67, the YCbCr signal 918 may be sent to the selection logic 922and/or to the memory 108. The YCbCr processing logic 904 may receive theinput signal 924, which may be YCbCr image data from the signal 918 orfrom the memory 108, as shown by signal 920, depending on theconfiguration of the selection logic 922. The YCbCr image data 924 maythen be processed by the YCbCr processing logic 904 for luma sharpening,chroma suppression, chroma noise reduction, chroma noise reduction, aswell as brightness, contrast, and color adjustments, and so forth.Further, the YCbCr processing logic 904 may provide for gamma mappingand scaling of the processed image data in both horizontal and verticaldirections.

A block diagram depicting a more detailed view of an embodiment of theYCbCr processing logic 904 is illustrated in FIG. 90. As shown, theYCbCr processing logic 904 includes the image sharpening logic 1170, thelogic 1172 for adjusting brightness, contrast, and/or color, the YCbCrgamma adjustment logic 1174, the chroma decimation logic 1176, and thescaling logic 1178. The YCbCr processing logic 904 may be configured toprocess pixel data in 4:4:4, 4:2:2, or 4:2:0 formats using 1-plane,2-plane, or 3-plane memory configurations. Further, in one embodiment,the YCbCr input signal 924 may provide luma and chroma information as10-bit values.

As will be appreciated, the reference to 1-plane, 2-plane, or 3-planerefers to the number of imaging planes utilized in picture memory. Forinstance, in a 3-plane format, each of the Y, Cb, and Cr components mayutilize separate respective memory planes. In a 2-plane format, a firstplane may be provided for the luma component (Y), and a second planethat interleaves the Cb and Cr samples may be provided for the chromacomponents (Cb and Cr). In a 1-plane format, a single plane in memory isinterleaved with the luma and chroma samples.

Further, with regard to the 4:4:4, 4:2:2, and 4:2:0 formats, it may beappreciated that the 4:4:4 format refers to a sampling format in whicheach of the three YCbCr components are sampled at the same rate. In a4:2:2 format, the chroma components Cb and Cr are sub-sampled at halfthe sampling rate of the luma component Y, thus reducing the resolutionof chroma components Cb and Cr by half in the horizontal direction.Similarly the 4:2:0 format subs-samples the chroma components Cb and Crin both the vertical and horizontal directions.

The processing of the YCbCr information may occur within an activesource region defined within a source buffer, wherein the active sourceregion contains “valid” pixel data. For example, referring to FIG. 91, asource buffer 1180 having defined therein an active source region 1182is illustrated. In the illustrated example, the source buffer mayrepresent a 4:4:4 1-plane format providing source pixels of 10-bitvalues. The active source region 1182 may be specified individually forluma (Y) samples and chroma samples (Cb and Cr). Thus, it should beunderstood that the active source region 1182 may actually includemultiple active source regions for the luma and chroma samples. Thestart of the active source regions 1182 for luma and chroma may bedetermined based on an offset from a base address (0,0) 1184 of thesource buffer. For instance, a starting position (Lm_X, Lm_Y) 1186 forthe luma active source region may be defined by an x-offset 1190 and ay-offset 1194 with respect to the base address 1184. Similarly, astarting position (Ch_X, Ch_Y) 1188 for the chroma active source regionmay be defined by an x-offset 1192 and a y-offset 1196 with respect tothe base address 1184. It should be noted that in the present example,the y-offsets 1192 and 1196 for luma and chroma, respectively, may beequal. Based on the starting position 1186, the luma active sourceregion may be defined by a width 1193 and a height 1200, each of whichmay represent the number of luma samples in the x and y directions,respectively. Additionally, based on the starting position 1188, thechroma active source region may be defined by a width 1202 and a height1204, each of which may represent the number of chroma samples in the xand y directions, respectively.

FIG. 92 further provides an example showing how active source regionsfor luma and chroma samples may be determined in a two-plane format. Forinstance, as shown, the luma active source region 1182 may be defined ina first source buffer 1180 (having the base address 1184) by the areaspecified by the width 1193 and height 1200 with respect to the startingposition 1186. A chroma active source region 1208 may be defined in asecond source buffer 1206 (having the base address 1184) as the areaspecified by the width 1202 and height 1204 relative to the startingposition 1188.

With the above points in mind and referring back to FIG. 90, the YCbCrsignal 924 is first received by the image sharpening logic 1170. Theimage sharpening logic 1170 may be configured to perform picturesharpening and edge enhancement processing to increase texture and edgedetails in the image. As will be appreciated, image sharpening mayimprove the perceived image resolution. However, it is generallydesirable that existing noise in the image is not detected as textureand/or edges, and thus not amplified during the sharpening process.

In accordance with the present technique, the image sharpening logic1170 may perform picture sharpening using a multi-scale unsharp maskfilter on the luma (Y) component of the YCbCr signal. In one embodiment,two or more low pass Gaussian filters of difference scale sizes may beprovided. For example, in an embodiment that provides two Gaussianfilters, the output (e.g., Gaussian blurring) of a first Gaussian filterhaving a first radius (x) is subtracted from the output of a secondGaussian filter having a second radius (y), wherein x is greater than y,to generate an unsharp mask. Additional unsharp masks may also beobtained by subtracting the outputs of the Gaussian filters from the Yinput. In certain embodiments, the technique may also provide adaptivecoring threshold comparison operations that may be performed using theunsharp masks such that, based upon the results of the comparison(s),gain amounts may be added to a base image, which may be selected as theoriginal Y input image or the output of one of the Gaussian filters, togenerate a final output.

Referring to FIG. 93, block diagram depicting exemplary logic 1210 forperforming image sharpening in accordance with embodiments of thepresently disclosed techniques is illustrated. The logic 1210 representsa multi-scale unsharp filtering mask that may be applied to an inputluma image Yin. For instance, as shown, Yin is received and processed bytwo low pass Gaussian filters 1212 (G1) and 1214 (G2). In the presentexample, the filter 1212 may be a 3×3 filter and the filter 1214 may bea 5×5 filter. It should be appreciated, however, that in additionalembodiments, more than two Gaussian filters, including filters ofdifferent scales may also be used (e.g., 7×7, 9×9, etc.). As will beappreciated, due to the low pass filtering process, the high frequencycomponents, which generally correspond to noise, may be removed from theoutputs of the G1 and G2 to produce “unsharp” images (G1out and G2out).As will be discussed below, using an unsharp input image as a base imageallows for noise reduction as part of the sharpening filter.

The 3×3 Gaussian filter 1212 and the 5×5 Gaussian filter 1214 may bedefined as shown below:

${G\; 1} = \frac{\begin{bmatrix}{G\; 1_{1}} & {G\; 1_{1}} & {G\; 1_{1}} \\{G\; 1_{1}} & {G\; 1_{0}} & {G\; 1_{1}} \\{G\; 1_{1}} & {G\; 1_{1}} & {G\; 1_{1}}\end{bmatrix}}{256}$ ${G\; 2} = \frac{\begin{bmatrix}{G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{0}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{1}} & {G\; 2_{2}} \\{G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}} & {G\; 2_{2}}\end{bmatrix}}{256}$

By way of example only, the values of the Gaussian filters G1 and G2 maybe selected in one embodiment as follows:

${G\; 1} = \frac{\begin{bmatrix}28 & 28 & 28 \\28 & 32 & 28 \\28 & 28 & 28\end{bmatrix}}{256}$ ${G\; 2} = \frac{\begin{bmatrix}9 & 9 & 9 & 9 & 9 \\9 & 12 & 12 & 12 & 9 \\9 & 12 & 16 & 12 & 9 \\9 & 12 & 12 & 12 & 9 \\9 & 9 & 9 & 9 & 9\end{bmatrix}}{256}$

Based on Yin, G1out, and G2out, three unsharp masks, Sharp1, Sharp2, andSharp3, may be generated. Sharp1 may be determined as the unsharp imageG2out of the Gaussian filter 1214 subtracted from the unsharp imageG1out of the Gaussian filter 1212. Because Sharp1 is essentially thedifference between two low pass filters, it may be referred to as a “midband” mask, since the higher frequency noise components are alreadyfiltered out in the G1out and G2out unsharp images. Additionally, Sharp2may be calculated by subtracting G2out from the input luma image Yin,and Sharp3 may be calculated by subtracting G1out from the input lumaimage Yin. As will be discussed below, an adaptive threshold coringscheme may be applied using the unsharp masks Sharp1, Sharp2, andSharp3.

Referring to the selection logic 1216, a base image may be selectedbased upon a control signal UnsharpSel. In the illustrated embodiment,the base image may be either the input image Yin, or the filteredoutputs G1out or G2out. As will be appreciated, when an original imageshas a high noise variance (e.g., almost as high as the signal variance),using the original image Yin as the base image in sharpening may notsufficiently provide for reduction of the noise components duringsharpening. Accordingly, when a particular threshold of noise content isdetected in the input image, the selection logic 1216 may be adapted toselect one of the low pass filtered outputs G1out or G2out from whichhigh frequency content, which may include noise, has been reduced. Inone embodiment, the value of the control signal UnsharpSel may bedetermined by analyzing statistical data acquired during statisticsprocessing in the ISP front-end block 80 to determine the noise contentof the image. By way of example, if the input image Yin has a low noisecontent, such that the appearance noise will likely not increase as aresult of the sharpening process, the input image Yin may be selected asthe base image (e.g., UnsharpSel=0). If the input image Yin isdetermined to contain a noticeable level of noise, such that thesharpening process may amplify the noise, one of the filtered imagesG1out or G2out may be selected (e.g., UnsharpSel=1 or 2, respectively).Thus, by applying an adaptive technique for selecting a base image, thelogic 1210 essentially provides a noise reduction function.

Next, gains may be applied to one or more of the Sharp1, Sharp2, andSharp3 masks in accordance with an adaptive coring threshold scheme, asdescribed below. Next, the unsharp values Sharp1, Sharp2, and Sharp3 maybe compared to various thresholds SharpThd1, SharpThd2, and SharpThd3(not necessarily respectively) by way of the comparator blocks 1218,1220, and 1222. For instance, Sharp1 value is always compared toSharpThd1 at the comparator block 1218. With respective to thecomparator block 1220, the threshold SharpThd2 may be compared againsteither Sharp1 or Sharp2, depending upon the selection logic 1226. Forinstance, the selection logic 1226 may select Sharp1 or Sharp2 dependingon the state of a control signal SharpCmp2 (e.g., SharpCmp2=1 selectsSharp1; SharpCmp2=0 selects Sharp2). For example, in one embodiment, thestate of SharpCmp2 may be determined depending on the noisevariance/content of the input image (Yin).

In the illustrated embodiment, it is generally preferable to set theSharpCmp2 and SharpCmp3 values to select Sharp1, unless it is detectedthat the image data has relatively low amounts of noise. This is becauseSharp1, being the difference between the outputs of the Gaussian lowpass filters G1 and G2, is generally less sensitive to noise, and thusmay help reduce the amount to which SharpAmt1, SharpAmt2, and SharpAmt3values vary due to noise level fluctuations in “noisy” image data. Forinstance, if the original image has a high noise variance, some of thehigh frequency components may not be caught when using fixed thresholdsand, thus, may be amplified during the sharpening process. Accordingly,if the noise content of the input image is high, then some of the noisecontent may be present in Sharp2. In such instances, SharpCmp2 may beset to 1 to select the mid-band mask Sharp1 which, as discussed above,has reduced high frequency content due to being the difference of twolow pass filter outputs and is thus less sensitive to noise.

As will be appreciated, a similar process may be applied to theselection of either Sharp1 or Sharp3 by the selection logic 1224 underthe control of SharpCmp3. In one embodiment, SharpCmp2 and SharpCmp3 maybe set to 1 by default (e.g., use Sharp1), and set to 0 only for thoseinput images that are identified as having generally low noisevariances. This essentially provides an adaptive coring threshold schemein which the selection of the comparison value (Sharp1, Sharp2, orSharp3) is adaptive based upon the noise variance of an input image.

Based on the outputs of the comparator blocks 1218, 1220, and 1222, thesharpened output image Ysharp may be determined by applying gainedunsharp masks to the base image (e.g., selected via logic 1216). Forinstance, referring first to the comparator block 1222, SharpThd3 iscompared to the B-input provided by selection logic 1224, which shall bereferred to herein as “SharpAbs,” and may be equal to either Sharp1 orSharp3 depending on the state of SharpCmp3. If SharpAbs is greater thanthe threshold SharpThd3, then a gain SharpAmt3 is applied to Sharp3, andthe resulting value is added to the base image. If SharpAbs is less thanthe threshold SharpThd3, then an attenuated gain Att3 may be applied. Inone embodiment, the attenuated gain Att3 may be determined as follows:

$\begin{matrix}{{{Att}\; 3} = \frac{{SharpAmt}\; 3 \times {SharpAbs}}{{SharpThd}\; 3}} & (104)\end{matrix}$

wherein, SharpAbs is either Sharp1 or Sharp3, as determined by theselection logic 1224. The selection of the based image summed witheither the full gain (SharpAmt3) or the attenuated gain (Att3) isperformed by the selection logic 1228 based upon the output of thecomparator block 1222. As will be appreciated, the use of an attenuatedgain may address situations in which SharpAbs is not greater than thethreshold (e.g., SharpThd3), but the noise variance of the image isnonetheless close to the given threshold. This may help to reducenoticeable transitions between a sharp and an unsharp pixel. Forinstance, if the image data is passed without the attenuated gain insuch circumstance, the resulting pixel may appear as a defective pixel(e.g., a stuck pixel).

Next, a similar process may be applied with respect to the comparatorblock 1220. For instance, depending on the state of SharpCmp2, theselection logic 1226 may provide either Sharp1 or Sharp2 as the input tothe comparator block 1220 that is compared against the thresholdSharpThd2. Depending on the output of the comparator block 1220, eitherthe gain SharpAmt2 or an attenuated gain based upon SharpAmt2, Att2, isapplied to Sharp2 and added to the output of the selection logic 1228discussed above. As will be appreciated, the attenuated gain Att2 may becomputed in a manner similar to Equation 104 above, except that the gainSharpAmt2 and the threshold SharpThd2 are applied with respect toSharpAbs, which may be selected as Sharp1 or Sharp2.

Thereafter, a gain SharpAmt1 or an attenuated gain Att1 is applied toSharp1, and the resulting value is summed with output of the selectionlogic 1230 to produce the sharpened pixel output Ysharp (from selectionlogic 1232). The selection of applying either the gain SharpAmt1 orattenuated gain Att1 may be determined based upon the output of thecomparator block 1218, which compares Sharp1 against the thresholdSharpThd1. Again, the attenuated gain Att1 may be determined in a mannersimilar to Equation 104 above, except that the gain SharpAmt1 andthreshold SharpThd1 are applied with respect to Sharp1. The resultingsharpened pixel values scaled using each of the three masks is added tothe input pixel Yin to generate the sharpened output Ysharp which, inone embodiment, may be clipped to 10 bits (assuming YCbCr processingoccurs at 10-bit precision).

As will be appreciated, when compared to conventional unsharp maskingtechniques, the image sharpening techniques set forth in this disclosuremay provide for improving the enhancement of textures and edges whilealso reducing noise in the output image. In particular, the presenttechniques may be well-suited in applications in which images capturedusing, for example, CMOS image sensors, exhibit poor signal-to-noiseratio, such as images acquired under low lighting conditions using lowerresolution cameras integrated into portable devices (e.g., mobilephones). For instance, when the noise variance and signal variance arecomparable, it is difficult to use a fixed threshold for sharpening, assome of the noise components would be sharpened along with texture andedges. Accordingly, the techniques provided herein, as discussed above,may filter the noise from the input image using multi-scale Gaussianfilters to extract features from the unsharp images (e.g., G1out andG2out) in order to provide a sharpened image that also exhibits reducednoise content.

Before continuing, it should be understood that the illustrated logic1210 is intended to provide only one exemplary embodiment of the presenttechnique. In other embodiments, additional or fewer features may beprovided by the image sharpening logic 1170. For instance, in someembodiments, rather than applying an attenuated gain, the logic 1210 maysimply pass the base value. Additionally, some embodiments may notinclude the selection logic blocks 1224, 1226, or 1216. For instance,the comparator blocks 1220 and 1222 may simply receive the Sharp2 andSharp3 values, respectively, rather than a selection output from theselection logic blocks 1224 and 1226, respectively. While suchembodiments may not provide for sharpening and/or noise reductionfeatures that are as robust as the implementation shown in FIG. 93, itshould be appreciated that such design choices may be the result of costand/or business related constraints.

In the present embodiment, the image sharpening logic 1170 may alsoprovide for edge enhancement and chroma suppression features once thesharpened image output YSharp is obtained. Each of these additionalfeatures will now be discussed below. Referring first to FIG. 94,exemplary logic 1234 for performing edge enhancement that may beimplemented downstream from the sharpening logic 1210 of FIG. 93 isillustrated in accordance with one embodiment. As shown, the originalinput value Yin is processed by a Sobel filter 1236 for edge detection.The Sobel filter 1236 may determine a gradient value YEdge based upon a3×3 pixel block (referred to as “A” below) of the original image, withYin being the center pixel of the 3×3 block. In one embodiment, theSobel filter 1236 may calculate YEdge by convolving the original imagedata to detect changes in horizontal and vertical directions. Thisprocess is shown below in Equations 105-107.

$\begin{matrix}{{S_{x} = \begin{bmatrix}1 & 0 & {- 1} \\2 & 0 & {- 2} \\1 & 0 & {- 1}\end{bmatrix}}{S_{y} = \begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix}}{{G_{x} = {S_{x} \times A}},}} & (105) \\{{G_{y} = {S_{y} \times A}},} & (106) \\{{{YEdge} = {G_{x} \times G_{y}}},} & (107)\end{matrix}$

wherein S_(x) and S_(y) are represent matrix operators for gradientedge-strength detection in the horizontal and vertical directions,respectively, and wherein G_(x) and G_(y) represent gradient images thatcontain horizontal and vertical change derivatives, respectively.Accordingly, the output YEdge is determined as the product of G_(x) andG_(y).

YEdge is then received by selection logic 1240 along with the mid-bandSharp1 mask, as discussed above in FIG. 93. Based on the control signalEdgeCmp, either Sharp1 or YEdge is compared to a threshold, EdgeThd, atthe comparator block 1238. The state of EdgeCmp may be determined, forexample, based upon the noise content of an image, thus providing anadaptive coring threshold scheme for edge detection and enhancement.Next, the output of the comparator block 1238 may be provided to theselection logic 1242 and either a full gain or an attenuated gain may beapplied. For instance, when the selected B-input to the comparator block1238 (Sharp1 or YEdge) is above EdgeThd, YEdge is multiplied by an edgegain, EdgeAmt, to determine the amount of edge enhancement that is to beapplied. If the B-input at the comparator block 1238 is less thanEdgeThd, then an attenuated edge gain, AttEdge, may be applied to avoidnoticeable transitions between the edge enhanced and original pixel. Aswill be appreciated, AttEdge may be calculated in a similar manner asshown in Equation 104 above, but wherein EdgeAmt and EdgeThd are appliedto “SharpAbs,” which may be Sharp1 or YEdge, depending on the output ofthe selection logic 1240. Thus, the edge pixel, enhanced using eitherthe gain (EdgeAmt) or the attenuated gain (AttEdge) may be added toYSharp (output of logic 1210 of FIG. 93) to obtain the edge-enhancedoutput pixel Yout which, in one embodiment, may be clipped to 10 bits(assuming YCbCr processing occurs at 10-bit precision).

With regard to chroma suppression features provided by the imagesharpening logic 1170, such features may attenuate chroma at luma edges.Generally, chroma suppression may be performed by applying a chroma gain(attenuation factor) of less than 1 depending on the value (YSharp,Yout) obtained from the luma sharpening and/or edge enhancement stepsdiscussed above. By way of example, FIG. 95 shows a graph 1250 thatincludes a curve 1252 representing chroma gains that may be selected forcorresponding sharpened luma values (YSharp). The data represented bythe graph 1250 may be implemented as a lookup table of YSharp values andcorresponding chroma gains between 0 and 1 (an attenuation factor). Thelookup tables are used to approximate the curve 1252. For YSharp valuesthat are co-located between two attenuation factors in the lookup table,linear interpolation may be applied to the two attenuation factorscorresponding to YSharp values above and below the current YSharp value.Further, in other embodiments, the input luma value may also be selectedas one of the Sharp1, Sharp2, or Sharp3 values determined by the logic1210, as discussed above in FIG. 93, or the YEdge value determined bythe logic 1234, as discussed in FIG. 94.

Next, the output of the image sharpening logic 1170 (FIG. 90) isprocessed by the brightness, contrast, and color (BCC) adjustment logic1172. A functional block diagram depicting an embodiment of the BCCadjustment logic 1172 is illustrated in FIG. 96. As shown the logic 1172includes a brightness and contrast processing block 1262, global huecontrol block 1264, and a saturation control block 1266. The presentlyillustrated embodiment provides for processing of the YCbCr data in10-bit precision, although other embodiments may utilize differentbit-depths. The functions of each of blocks 1262, 1264, and 1266 arediscussed below.

Referring first to the brightness and contrast processing block 1262, anoffset, YOffset, is first subtracted from the luma (Y) data to set theblack level to zero. This is done to ensure that the contrast adjustmentdoes not alter the black levels. Next, the luma value is multiplied by acontrast gain value to apply contrast control. By way of example, thecontrast gain value may be a 12-bit unsigned with 2 integer bits and 10fractional bits, thus providing for a contrast gain range of up to 4times the pixel value. Thereafter, brightness adjustment may beimplemented by adding (or subtracting) a brightness offset value fromthe luma data. By way of example, the brightness offset in the presentembodiment may be a 10-bit two's complement value having a range ofbetween −512 to +512. Further, it should be noted that brightnessadjustment is performed subsequent to contrast adjustment in order toavoid varying the DC offset when changing contrast. Thereafter, theinitial YOffset is added back to the adjusted luma data to re-positionthe black level.

Blocks 1264 and 1266 provide for color adjustment based upon huecharacteristics of the Cb and Cr data. As shown, an offset of 512(assuming 10-bit processing) is first subtracted from the Cb and Cr datato position the range to approximately zero. The hue is then adjusted inaccordance with the following equations:

Cb _(adj) =Cb cos(θ)+Cr sin(θ),  (108)

Cr _(adj) =Cr cos(θ)−Cb sin(θ),  (109)

wherein Cb_(adj) and Cr_(adj) represent adjusted Cb and Cr values, andwherein θ represents a hue angle, which may be calculated as follows:

$\begin{matrix}{\theta = {\arctan \left( \frac{Cr}{Cb} \right)}} & (110)\end{matrix}$

The above operations are depicted by the logic within the global huecontrol block 1264, and may be represented by the following matrixoperation:

$\begin{matrix}{{\begin{bmatrix}{Cb}_{adj} \\{Cr}_{adj}\end{bmatrix} = {\begin{bmatrix}{Ka} & {Kb} \\{- {Kb}} & {Ka}\end{bmatrix}\begin{bmatrix}{Cb} \\{Cr}\end{bmatrix}}},} & (111)\end{matrix}$

wherein, Ka=cos(θ), Kb=sin(θ), and θ is defined above in Equation 110.

Next, saturation control may be applied to the Cb_(adj) and Cr_(adj)values, as shown by the saturation control block 1266. In theillustrated embodiment, saturation control is performed by applying aglobal saturation multiplier and a hue-based saturation multiplier foreach of the Cb and Cr values. Hue-based saturation control may improvethe reproduction of colors. The hue of the color may be represented inthe YCbCr color space, as shown by the color wheel graph 1270 in FIG.97. As will be appreciated, the YCbCr hue and saturation color wheel1270 may be derived by shifting the identical color wheel in the HSVcolor space (hue, saturation, and intensity) by approximately 109degrees. As shown, the graph 1270 includes circumferential valuesrepresenting the saturation multiplier (S) within a range of 0 to 1, aswell as angular values representing θ, as defined above, within a rangeof between 0 to 360°. Each θ may represent a different color (e.g.,49°=magenta, 109°=red, 229°=green, etc.). The hue of the color at aparticular hue angle θ may be adjusted by selecting an appropriatesaturation multiplier S.

Referring back to FIG. 96, the hue angle θ (calculated in the global huecontrol block 1264) may be used as an index for a Cb saturation lookuptable 1268 and a Cr saturation lookup table 1269. In one embodiment, thesaturation lookup tables 1268 and 1269 may contain 256 saturation valuesdistributed evenly in the hue range from 0-360° (e.g., the first lookuptable entry is at 0° and the last entry is at 360°) and the saturationvalue S at a given pixel may be determined via linear interpolation ofsaturation values in the lookup table just below and above the currenthue angle θ. A final saturation value for each of the Cb and Crcomponents is obtained by multiplying a global saturation value (whichmay be a global constant for each of Cb and Cr) with the determinedhue-based saturation value. Thus, the final corrected Cb′ and Cr′ valuesmay be determined by multiplying Cb_(adj) and Cr_(adj) with theirrespective final saturation values, as shown in the hue-based saturationcontrol block 1266.

Thereafter, the output of the BCC logic 1172 is passed to the YCbCrgamma adjustment logic 1174, as shown in FIG. 90. In one embodiment, thegamma adjustment logic 1174 may provide non-linear mapping functions forthe Y, Cb and Cr channels. For instance, the input Y, Cb, and Cr valuesare mapped to corresponding output values. Again, assuming that theYCbCr data is processed in 10-bits, an interpolated 10-bit 256 entrylookup table may be utilized. Three such lookup tables may be providedwith one for each of the Y, Cb, and Cr channels. Each of the 256 inputentries may be evenly distributed and, an output may be determined bylinear interpolation of the output values mapped to the indices justabove and below the current input index. In some embodiments, anon-interpolated lookup table having 1024 entries (for 10-bit data) mayalso be used, but may have significantly greater memory requirements. Aswill be appreciated, by adjusting the output values of the lookuptables, the YCbCr gamma adjustment function may be also be used toperform certain image filter effects, such as black and white, sepiatone, negative images, solarization, and so forth.

Next, chroma decimation may be applied by the chroma decimation logic1176 to the output of the gamma adjustment logic 1174. In oneembodiment, the chroma decimation logic 1176 may be configured toperform horizontal decimation to convert the YCbCr data from a 4:4:4format to a 4:2:2 format, in which the chroma (Cr and Cr) information issub-sampled at half rate of the luma data. By way of example only,decimation may be performed by applying a 7-tap low pass filter, such asa half-band lanczos filter, to a set of 7 horizontal pixels, as shownbelow:

$\begin{matrix}{{{Out} = \frac{\begin{matrix}{{C\; 0 \times {{in}\left( {i - 3} \right)}} + {C\; 1 \times {{in}\left( {i - 2} \right)}} + {C\; 2 \times {{in}\left( {i - 1} \right)}} + {C\; 3 \times}} \\{{{in}(i)} + {C\; 4 \times {{in}\left( {i + 1} \right)}} + {C\; 5 \times {{in}\left( {i + 2} \right)}} + {C\; 6 \times {{in}\left( {i + 3} \right)}}}\end{matrix}}{512}},} & (112)\end{matrix}$

wherein in(i) represents the input pixel (Cb or Cr), and C0-C6 representthe filtering coefficients of the 7-tap filter. Each input pixel has anindependent filter coefficient (C0-C6) to allow flexible phase offsetfor the chroma filtered samples.

Further, chroma decimation may, in some instances, also be performedwithout filtering. This may be useful when the source image wasoriginally received in 4:2:2 format, but was up-sampled to 4:4:4 formatfor YCbCr processing. In this case, the resulting decimated 4:2:2 imageis identical to the original image.

Subsequently, the YCbCr data output from the chroma decimation logic1176 may be scaled using the scaling logic 1178 prior to being outputfrom the YCbCr processing block 904. The function of the scaling logic1178 may be similar to the functionality of the scaling logic 368, 370in the binning compensation filter 300 of the front-end pixel processingunit 130, as discussed above with reference to FIG. 28. For instance,the scaling logic 1178 may perform horizontal and vertical scaling astwo steps. In one embodiment, a 5-tap polyphase filter may be used forvertical scaling, and a 9-tap polyphase filter may be used forhorizontal scaling. The multi-tap polyphase filters may multiply pixelsselected from the source image by a weighting factor (e.g., filtercoefficient), and then sum the outputs to form the destination pixel.The selected pixels may be chosen depending on the current pixelposition and the number of filters taps. For instance, with a vertical5-tap filter, two neighboring pixels on each vertical side of a currentpixel may be selected and, with a horizontal 9-tap filter, fourneighboring pixels on each horizontal side of the current pixel may beselected. The filtering coefficients may be provided from a lookuptable, and may be determined by the current between-pixel fractionalposition. The output 926 of the scaling logic 1178 is then output fromthe YCbCr processing block 904.

Returning back to FIG. 67, the processed output signal 926 may be sentto the memory 108, or may be output from the ISP pipe processing logic82 as the image signal 114 to display hardware (e.g., display 28) forviewing by a user, or to a compression engine (e.g., encoder 118). Insome embodiments, the image signal 114 may be further processed by agraphics processing unit and/or a compression engine and stored beforebeing decompressed and provided to a display. Additionally, one or moreframe buffers may also be provided to control the buffering of the imagedata being output to a display, particularly with respect to video imagedata.

As will be understood, the various image processing techniques describedabove and relating to defective pixel detection and correction, lensshading correction, demosaicing, and image sharpening, among others, areprovided herein by way of example only. Accordingly, it should beunderstood that the present disclosure should not be construed as beinglimited to only the examples provided above. Indeed, the exemplary logicdepicted herein may be subject to a number of variations and/oradditional features in other embodiments. Further, it should beappreciated that the above-discussed techniques may be implemented inany suitable manner. For instance, the components of the imageprocessing circuitry 32, and particularly the ISP front-end block 80 andthe ISP pipe block 82 may be implemented using hardware (e.g., suitablyconfigured circuitry), software (e.g., via a computer program includingexecutable code stored on one or more tangible computer readablemedium), or via using a combination of both hardware and softwareelements.

The specific embodiments described above have been shown by way ofexample, and it should be understood that these embodiments may besusceptible to various modifications and alternative forms. It should befurther understood that the claims are not intended to be limited to theparticular forms disclosed, but rather to cover all modifications,equivalents, and alternatives falling within the spirit and scope ofthis disclosure.

What is claimed is:
 1. An image signal processing system comprising: afront-end pixel processing unit configured to receive a frame of rawimage data comprising pixels acquired using a digital image sensor,wherein the front-end pixel processing unit comprises statisticscollection logic configured to collect statistics based upon the rawframe pixel data, wherein the collected statistics comprises at leastone of auto-white balance statistics, auto-exposure statistics,auto-focus statistics, and flicker detection statistics, and wherein thecollection of the statistics occurs prior to the raw frame beingprocessed by an image signal processing pipeline coupled downstream ofthe front-end pixel processing unit.
 2. The image signal processingsystem of claim 1, wherein the statistics collection logic comprises: aninput configured to receive the raw frame pixels; color space conversionlogic configured to convert the raw frame pixel data into multiple setsof converted pixel data, each of the converted pixel data sets being ina different color space; and a set of pixel filters, each beingconfigured to receive the raw frame pixel data and the multiple sets ofconverted pixel data, to select one set of either the raw frame pixeldata or the converted pixel data, and to determine one or moreaccumulated color sum values by analyzing the selected set of pixeldata.
 3. The image signal processing system of claim 2, wherein themultiple sets of converted pixel data include at least one set of pixeldata in a non-linear color space and one set of pixel data in a linearcolor space.
 4. The image signal processing system of claim 3, whereinthe non-linear color space comprises at least one of a sRGB color space,a sRGB_(linear) color space, or a YC1C2 color space.
 5. The image signalprocessing system of claim 3, wherein the linear color space comprises acamera color space conversion.
 6. The image signal processing system ofclaim 2, comprising down-scaling logic configured to down-scale the rawframe pixel data before the raw frame pixel data is provided to thecolor space conversion logic.
 7. The image signal processing system ofclaim 6, wherein the raw frame pixel data comprises Bayer RGB data, andwherein the down-scaling logic down-scales the Bayer RGB data byaveraging a 4×4 block of pixels comprising 2×2 Bayer quads.
 8. A methodcomprising: receiving a frame of raw image data acquired using a digitalimage sensor; using a statistics collection engine of an image signalprocessor to apply a linear camera color space conversion to the rawimage data to generated a converted set of pixel data in the cameracolor space; accumulating a luma value over each row of the raw frame todetermine a luma row sum; and applying frequency analysis to the lumarow sum to detect whether flicker is present in the raw image data. 9.The method of claim 8, wherein the flicker is the result of a periodicvariation in an illuminant source powered by AC power.
 10. The method ofclaim 9, wherein applying frequency analysis comprises analyzing the rowaverage of the luma row sum to determine a frequency of the AC power.11. The method of claim 9, wherein applying frequency analysis comprisesanalyzing row average differences of consecutive image frames todetermine a frequency of the AC power.
 12. The method of claim 9,comprising determining a flicker period corresponding to a frequency ofthe AC power.
 13. The method of claim 12, comprising reducing theappearance of flicker by adjusting the integration time of the digitalimage sensor to be an integer multiple of the determined flicker period.14. A method comprising using an image signal processor (ISP): receivinga frame of raw image data acquired using a digital image sensor; andconverting the raw image data into a non-linear luma and chroma colorspace and a camera luma and chroma color space; selecting one of anon-linear luma and chroma color space and a camera luma and chromacolor space; and analyzing the raw frame using a statistics collectionengine of a front-end pixel processing unit of the ISP to generate atwo-dimensional color histogram for a window within a raw frame, whereinthe two-dimensional color histogram is in the selected luma and chromacolor space and includes a plurality of bins arranged at fixedintervals.
 15. The method of claim 14, wherein generating thetwo-dimensional color histogram in the selected luma and chroma colorspace comprises: for a current luma and chroma pixel data set,determining a first bin index for a first chroma component anddetermining a second bin index for a second chroma component; andincrementing a bin corresponding to the first and second bin indices bya count value if the first and second bin indices are within a rangecorresponding to the number of bins defining a height and width,respectively, of the window.
 16. The method of claim 15, wherein, foreach pixel in the current luma and chroma pixel data set, the countvalue for each of the chroma components for a current pixel is weightedbased on a luma component associated with the current pixel.
 17. Themethod of claim 15, wherein the count value for each of the chromacomponents is weighted by evaluating the value of the luma component ofthe current pixel relative to a plurality of luma intervals defined byone or more luma thresholds, and wherein the count value for each of thechroma components of the current pixel is weighted more heavily in itscorresponding bin depending on the brightness of the current pixelincreases, as indicated by the luma interval corresponding to the lumacomponent associated with the current pixel.
 18. The method of claim 15,wherein the first chroma component and the second chroma component arerepresented by 8-bit values, and wherein the first and second binindices are determined based at least partially upon the upper five bitsof the first and second chroma components.
 19. An electronic devicecomprising: a digital image sensor; an interface configured tocommunicate with the digital image sensor; a memory device; a displaydevice configured to display a visual representation of an image scenecorresponding to raw image data acquired by the digital image sensor;and an image signal processing sub-system comprising a front-end pixelprocessing unit having defective pixel detection and correction logic,black level compensation logic, and statistics collection logic, whereinthe statistics collection logic is configured to receive the raw imagedata after the raw image data is processed by the defective pixeldetection and correction logic and the black level compensation logicand to collect one or more sets of statistics from the raw image datarelating to one or more of auto-white balance, flicker detection,auto-focus, and auto-exposure; wherein the one or more sets ofstatistics comprises tile statistics collected for one window, floatingwindow statistics for auto-white balance and auto-exposure, luma row sumvalues, two-dimensional color histogram, one or more componenthistograms, and one or more sets of edge sum values for auto-focusstatistics.
 20. The electronic device of claim 19, wherein the one ormore sets of statistics are written to the memory device using a DMAinterface.
 21. The electronic device of claim 20, wherein the memorydevice comprises double-buffered memory address registers configured toallow for a new location in the memory device to be specified for eachimage frame.
 22. The electronic device of claim 19, wherein the rawimage data comprises Bayer RGB pixel data, and wherein the electronicdevice comprises color space conversion logic configured to convert theBayer RGB pixel data into a corresponding set of pixels in asRGB_(linear) color space using a first linear transform, to convert thesRGB_(linear) pixels into a corresponding set of pixels in a sRGB colorspace using a non-linear look-up table, and to convert the sRGB pixelsinto a corresponding set of pixels in a luma and chroma color spaceusing a second linear transform.
 23. The electronic device of claim 22,wherein the one or more component histograms comprises a first histogramand a second histogram, wherein the first histogram comprises componentsselected from one of the Bayer RGB pixel data received by the statisticscollection logic, the sRGB_(linear) pixels, the sRGB pixels, or the lumaand chroma pixels, and wherein the second histogram comprises componentsselected from Bayer RGB data received by the front-end pixel processingunit that has not been processed by the defective pixel detection andcorrection logic and the black level compensation logic.
 24. Theelectronic device of claim 23, wherein the second histogram is used bythe black level compensation logic to determine an offset for applyingblack level compensation to the Bayer RGB data received by the front-endpixel processing unit.
 25. The electronic device of claim 19, whereindigital image sensor comprises at least one of a digital cameraintegrated with the electronic device, an external digital cameracoupled to the electronic device via the interface, or some combinationthereof.
 26. The electronic device of claim 19, wherein the sensorinterface comprises a Standard Mobile Imaging Architecture (SMIA)interface.
 27. The electronic device of claim 19, comprising at leastone of a desktop computer, a laptop computer, a tablet computer, amobile cellular telephone, a portable media player, or any combinationthereof.